import argparse import datetime import json import os import random import time from collections import OrderedDict from functools import partial from pathlib import Path import deepspeed import numpy as np import torch import torch.backends.cudnn as cudnn from timm.data.mixup import Mixup from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy from timm.models import create_model from timm.utils import ModelEma # NOTE: Do not comment `import models`, it is used to register models import models # noqa: F401 import utils from dataset import build_dataset from engine_for_finetuning import ( final_test, merge, train_one_epoch, validation_one_epoch, ) from optim_factory import ( LayerDecayValueAssigner, create_optimizer, get_parameter_groups, ) from utils import NativeScalerWithGradNormCount as NativeScaler from utils import multiple_samples_collate def get_args(): parser = argparse.ArgumentParser( 'VideoMAE fine-tuning and evaluation script for action 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=100, type=int) # Model parameters parser.add_argument( '--model', default='vit_base_patch16_224', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--tubelet_size', type=int, default=2) parser.add_argument( '--input_size', default=224, type=int, help='images input size') parser.add_argument( '--with_checkpoint', action='store_true', default=False) 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( '--head_drop_rate', type=float, default=0.0, metavar='PCT', help='cls head dropout rate (default: 0.)') 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=1e-3, metavar='LR', help='learning rate (default: 1e-3)') parser.add_argument('--layer_decay', type=float, default=0.75) parser.add_argument( '--warmup_lr', type=float, default=1e-8, 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( '--num_sample', type=int, default=2, help='Repeated_aug (default: 2)') parser.add_argument( '--aa', type=str, default='rand-m7-n4-mstd0.5-inc1', metavar='NAME', help= 'Use AutoAugment policy. "v0" or "original". " + "(default: rand-m7-n4-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) parser.add_argument('--short_side_size', type=int, default=224) parser.add_argument('--test_num_segment', type=int, default=10) parser.add_argument('--test_num_crop', type=int, default=3) # * 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=0.8, help='mixup alpha, mixup enabled if > 0.') parser.add_argument( '--cutmix', type=float, default=1.0, 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') 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') # Dataset parameters parser.add_argument( '--data_path', default='/your/data/path/', type=str, help='dataset path') parser.add_argument( '--data_root', default='', type=str, help='dataset path root') parser.add_argument( '--eval_data_path', default=None, type=str, help='dataset path for evaluation') parser.add_argument( '--nb_classes', default=400, type=int, help='number of the classification types') parser.add_argument( '--imagenet_default_mean_and_std', default=True, action='store_true') parser.add_argument('--num_segments', type=int, default=1) parser.add_argument('--num_frames', type=int, default=16) parser.add_argument('--sampling_rate', type=int, default=4) parser.add_argument('--sparse_sample', default=False, action='store_true') parser.add_argument( '--data_set', default='Kinetics-400', choices=[ 'Kinetics-400', 'Kinetics-600', 'Kinetics-700', 'SSV2', 'UCF101', 'HMDB51', 'Diving48', 'Kinetics-710', 'MIT', 'basketball', 'basketball_three' ], type=str, help='dataset') parser.add_argument( '--fname_tmpl', default='img_{:05}.jpg', type=str, help='filename_tmpl for rawframe dataset') parser.add_argument( '--start_idx', default=1, type=int, help='start_idx for rwaframe dataset') 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( '--validation', action='store_true', help='Perform validation 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) known_args, _ = parser.parse_known_args() if known_args.enable_deepspeed: parser = deepspeed.add_config_arguments(parser) ds_init = deepspeed.initialize 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, test_mode=False, args=args) if args.disable_eval_during_finetuning: dataset_val = None else: dataset_val, _ = build_dataset( is_train=False, test_mode=False, args=args) dataset_test, _ = build_dataset(is_train=False, test_mode=True, args=args) num_tasks = utils.get_world_size() global_rank = utils.get_rank() 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) sampler_test = torch.utils.data.DistributedSampler( dataset_test, num_replicas=num_tasks, rank=global_rank, shuffle=False) else: sampler_val = torch.utils.data.SequentialSampler(dataset_val) 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 if args.num_sample > 1: collate_func = partial(multiple_samples_collate, fold=False) else: collate_func = None 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, collate_fn=collate_func, persistent_workers=True) if dataset_val is not None: 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, persistent_workers=True) else: data_loader_val = None if dataset_test is not None: data_loader_test = torch.utils.data.DataLoader( dataset_test, sampler=sampler_test, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False, persistent_workers=True) else: data_loader_test = 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) model = create_model( args.model, img_size=args.input_size, pretrained=False, num_classes=args.nb_classes, all_frames=args.num_frames * args.num_segments, tubelet_size=args.tubelet_size, drop_rate=args.drop, drop_path_rate=args.drop_path, attn_drop_rate=args.attn_drop_rate, head_drop_rate=args.head_drop_rate, drop_block_rate=None, use_mean_pooling=args.use_mean_pooling, init_scale=args.init_scale, with_cp=args.with_checkpoint, ) patch_size = model.patch_embed.patch_size print("Patch size = %s" % str(patch_size)) args.window_size = (args.num_frames // args.tubelet_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=True) 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 for old_key in list(checkpoint_model.keys()): if old_key.startswith('_orig_mod.'): new_key = old_key[10:] checkpoint_model[new_key] = checkpoint_model.pop(old_key) state_dict = model.state_dict() all_keys = list(checkpoint_model.keys()) new_dict = OrderedDict() for key in all_keys: if key.startswith('backbone.'): new_dict[key[9:]] = checkpoint_model[key] elif key.startswith('encoder.'): new_dict[key[8:]] = checkpoint_model[key] else: new_dict[key] = checkpoint_model[key] checkpoint_model = new_dict # interpolate position embedding if 'pos_embed' in checkpoint_model: pos_embed_checkpoint = checkpoint_model['pos_embed'] embedding_size = pos_embed_checkpoint.shape[-1] # channel dim num_patches = model.patch_embed.num_patches # num_extra_tokens = model.pos_embed.shape[-2] - num_patches # 0/1 # height (== width) for the checkpoint position embedding orig_size = int( ((pos_embed_checkpoint.shape[-2] - num_extra_tokens) // (args.num_frames // model.patch_embed.tubelet_size))**0.5) # height (== width) for the new position embedding new_size = int( (num_patches // (args.num_frames // model.patch_embed.tubelet_size))**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:] # B, L, C -> BT, H, W, C -> BT, C, H, W pos_tokens = pos_tokens.reshape( -1, args.num_frames // model.patch_embed.tubelet_size, orig_size, orig_size, embedding_size) 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) # BT, C, H, W -> BT, H, W, C -> B, T, H, W, C pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape( -1, args.num_frames // model.patch_embed.tubelet_size, new_size, new_size, embedding_size) pos_tokens = pos_tokens.flatten(1, 3) # B, L, C new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed elif args.input_size != 224: pos_tokens = model.pos_embed org_num_frames = 16 T = org_num_frames // args.tubelet_size P = int((pos_tokens.shape[1] // T)**0.5) C = pos_tokens.shape[2] new_P = args.input_size // patch_size[0] # print(f'stats: {T} {P} {C}') # print(pos_tokens.shape) # B, L, C -> BT, H, W, C -> BT, C, H, W pos_tokens = pos_tokens.reshape(-1, T, P, P, C) pos_tokens = pos_tokens.reshape(-1, P, P, C).permute(0, 3, 1, 2) pos_tokens = torch.nn.functional.interpolate( pos_tokens, size=(new_P, new_P), mode='bicubic', align_corners=False) # BT, C, H, W -> BT, H, W, C -> B, T, H, W, C pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, T, new_P, new_P, C) pos_tokens = pos_tokens.flatten(1, 3) # B, L, C model.pos_embed = pos_tokens # update utils.load_state_dict( model, checkpoint_model, prefix=args.model_prefix) 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 * num_tasks num_training_steps_per_epoch = len(dataset_train) // total_batch_size args.lr = args.lr * total_batch_size / 256 #########scale the lr############# args.min_lr = args.min_lr * total_batch_size / 256 args.warmup_lr = args.warmup_lr * total_batch_size / 256 #########scale the lr############# 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" % len(dataset_train)) print("Number of training training per epoch = %d" % num_training_steps_per_epoch) num_layers = model_without_ddp.get_num_layers() 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() print("Skip weight decay list: ", skip_weight_decay_list) 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: print(f'No Deepspeed') if args.distributed: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.gpu], find_unused_parameters=False) model_without_ddp = model.module print(f'Building optimizer') 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))) 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.validation: test_stats = validation_one_epoch(data_loader_val, model, device) print( #f"{len(dataset_val)} val images: Top-1 {test_stats['acc1']:.2f}%, Top-5 {test_stats['acc5']:.2f}%, loss {test_stats['loss']:.4f}" f"{len(dataset_val)} val images: Top-1 {test_stats['acc1']:.2f}%, loss {test_stats['loss']:.4f}" ) exit(0) if args.eval: preds_file = os.path.join(args.output_dir, str(global_rank) + '.txt') test_stats = final_test(data_loader_test, model, device, preds_file) torch.distributed.barrier() # if global_rank == 0: # print("Start merging results...") # final_top1, final_top5 = merge(args.output_dir, num_tasks) # print( # f"Accuracy of the network on the {len(dataset_test)} test videos: Top-1: {final_top1:.2f}%, Top-5: {final_top5:.2f}%" # ) # log_stats = {'Final top-1': final_top1, 'Final Top-5': final_top5} # if args.output_dir and utils.is_main_process(): # with open( # os.path.join(args.output_dir, "log.txt"), # mode="a", # encoding="utf-8") as f: # f.write(json.dumps(log_stats) + "\n") 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: data_loader_train.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: _epoch = epoch + 1 if _epoch % args.save_ckpt_freq == 0 or _epoch == 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: test_stats = validation_one_epoch(data_loader_val, model, device) print( f"Accuracy of the network on the {len(dataset_val)} val images: {test_stats['acc1']:.2f}%" ) 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( val_acc1=test_stats['acc1'], head="perf", step=epoch) # log_writer.update( # val_acc5=test_stats['acc5'], head="perf", step=epoch) log_writer.update( val_loss=test_stats['loss'], head="perf", step=epoch) log_stats = { **{f'train_{k}': v for k, v in train_stats.items()}, **{f'val_{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()}, '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") preds_file = os.path.join(args.output_dir, str(global_rank) + '.txt') test_stats = final_test(data_loader_test, model, device, preds_file) torch.distributed.barrier() if global_rank == 0: print("Start merging results...") final_top1, final_top5 = merge(args.output_dir, num_tasks) print( f"Accuracy of the network on the {len(dataset_test)} test videos: Top-1: {final_top1:.2f}%" ) log_stats = {'Final top-1': final_top1} if args.output_dir and utils.is_main_process(): 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)