import argparse import datetime import numpy as np import time import torch import torch.backends.cudnn as cudnn import json import os from functools import partial from pathlib import Path from collections import OrderedDict from datasets.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 from datasets import build_dataset from engines.engine_for_finetuning import train_one_epoch, validation_one_epoch, final_test, merge from utils import NativeScalerWithGradNormCount as NativeScaler from utils import multiple_samples_collate import utils import contextlib from models import * def get_args(): parser = argparse.ArgumentParser('VideoMAE fine-tuning and evaluation script for video 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('--orig_t_size', type=int, default=8) parser.add_argument('--input_size', default=224, type=int, help='videos input size') parser.add_argument('--use_learnable_pos_emb', action='store_true') parser.set_defaults(use_learnable_pos_emb=False) parser.add_argument('--fc_drop_rate', type=float, default=0.0, metavar='PCT', help='Dropout rate (default: 0.)') 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=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-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-6)') 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=5) 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 (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('--delete_head', action='store_true', help='whether delete head') 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_checkpoint', action='store_true') parser.set_defaults(use_checkpoint=False) parser.add_argument('--checkpoint_num', default=0, type=int, help='number of layers for using checkpoint') 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('--prefix', default='', type=str, help='prefix for data') parser.add_argument('--split', default=' ', type=str, help='split for metadata') parser.add_argument('--filename_tmpl', default='img_{:05}.jpg', type=str, help='file template') parser.add_argument('--data_path', default='you_data_path', 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=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('--use_decord', action='store_true', help='whether use decord to load video, otherwise load image') parser.add_argument('--no_use_decord', action='store_false', dest='use_decord') parser.set_defaults(use_decord=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('--trimmed', type=int, default=60) parser.add_argument('--time_stride', type=int, default=16) parser.add_argument('--data_set', default='Kinetics', choices=[ 'Kinetics', 'Kinetics_sparse', 'SSV2', 'UCF101', 'HMDB51', 'image_folder', 'mitv1_sparse', 'LVU', 'COIN', 'Breakfast', 'basketball', 'basketball_three' ], type=str, help='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('--test_best', action='store_true', help='Whether test the best model') 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) parser.add_argument('--no_amp', action='store_true') parser.set_defaults(no_amp=False) # 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('--bf16', default=False, action='store_true') 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'") 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, 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) if 'deit' in args.model: model = create_model( args.model, pretrained=True, num_classes=args.nb_classes, fc_drop_rate=args.fc_drop_rate, drop_path_rate=args.drop_path, kernel_size=args.tubelet_size, num_frames=args.num_frames, ) elif 'videomamba' in args.model: model = create_model( args.model, img_size=args.input_size, pretrained=False if args.finetune else True, num_classes=args.nb_classes, fc_drop_rate=args.fc_drop_rate, drop_path_rate=args.drop_path, kernel_size=args.tubelet_size, num_frames=args.num_frames, use_checkpoint=args.use_checkpoint, checkpoint_num=args.checkpoint_num, ) else: model = create_model( args.model, pretrained=False, num_classes=args.nb_classes, all_frames=args.num_frames * args.num_segments, tubelet_size=args.tubelet_size, use_learnable_pos_emb=args.use_learnable_pos_emb, fc_drop_rate=args.fc_drop_rate, drop_rate=args.drop, drop_path_rate=args.drop_path, attn_drop_rate=args.attn_drop_rate, drop_block_rate=None, use_checkpoint=args.use_checkpoint, checkpoint_num=args.checkpoint_num, use_mean_pooling=args.use_mean_pooling, init_scale=args.init_scale, ) 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 if 'head.weight' in checkpoint_model.keys(): if args.delete_head: print("Removing head from pretrained checkpoint") del checkpoint_model['head.weight'] del checkpoint_model['head.bias'] elif checkpoint_model['head.weight'].shape[0] == 710: if args.nb_classes == 400: checkpoint_model['head.weight'] = checkpoint_model['head.weight'][:args.nb_classes] checkpoint_model['head.bias'] = checkpoint_model['head.bias'][:args.nb_classes] elif args.nb_classes in [600, 700]: # download from https://drive.google.com/drive/folders/17cJd2qopv-pEG8NSghPFjZo1UUZ6NLVm map_path = f'k710/label_mixto{args.nb_classes}.json' print(f'Load label map from {map_path}') with open(map_path) as f: label_map = json.load(f) checkpoint_model['head.weight'] = checkpoint_model['head.weight'][label_map] checkpoint_model['head.bias'] = checkpoint_model['head.bias'][label_map] 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 'deit' in args.model or 'videomamba' in args.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 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) 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 -> B, H, W, C -> B, C, H, W 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) # B, C, H, W -> B, H, W, C -> B, H, W, C pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, new_size, new_size, embedding_size) pos_tokens = pos_tokens.flatten(1, 2) # B, L, C new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed # we use 8 frames for pretraining temporal_pos_embed = checkpoint_model['temporal_pos_embedding'] orig_t_size = args.orig_t_size // model.patch_embed.tubelet_size new_t_size = args.num_frames // model.patch_embed.tubelet_size # height (== width) for the checkpoint position embedding if orig_t_size != new_t_size: print(f"Temporal interpolate from {orig_t_size} to {new_t_size}") temporal_pos_embed = temporal_pos_embed.permute(0, 2, 1) temporal_pos_embed = torch.nn.functional.interpolate( temporal_pos_embed, size=(new_t_size,), mode='linear', align_corners=False ) temporal_pos_embed = temporal_pos_embed.permute(0, 2, 1) checkpoint_model['temporal_pos_embedding'] = temporal_pos_embed elif '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 # we use 8 frames for pretraining orig_t_size = args.orig_t_size // model.patch_embed.tubelet_size new_t_size = args.num_frames // model.patch_embed.tubelet_size # height (== width) for the checkpoint position embedding orig_size = int(((pos_embed_checkpoint.shape[-2] - num_extra_tokens)//(orig_t_size)) ** 0.5) # height (== width) for the new position embedding new_size = int((num_patches // new_t_size) ** 0.5) if orig_t_size != new_t_size: print(f"Temporal interpolate from {orig_t_size} to {new_t_size}") tmp_pos_embed = pos_embed_checkpoint.view(1, orig_t_size, -1, embedding_size) tmp_pos_embed = tmp_pos_embed.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size) tmp_pos_embed = torch.nn.functional.interpolate(tmp_pos_embed, size=new_t_size, mode='linear') tmp_pos_embed = tmp_pos_embed.view(1, -1, embedding_size, new_t_size) tmp_pos_embed = tmp_pos_embed.permute(0, 3, 1, 2).reshape(1, -1, embedding_size) checkpoint_model['pos_embed'] = tmp_pos_embed pos_embed_checkpoint = tmp_pos_embed # 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, new_t_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, new_t_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 utils.load_state_dict(model, checkpoint_model, prefix=args.model_prefix) model.to(device) model_ema = None if args.model_ema: 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 = len(dataset_train) // total_batch_size args.lr = args.lr * total_batch_size * args.num_sample / 256 args.min_lr = args.min_lr * total_batch_size * args.num_sample / 256 args.warmup_lr = args.warmup_lr * total_batch_size * args.num_sample / 256 print("LR = %.8f" % args.lr) print("Batch size = %d" % total_batch_size) print("Repeated sample = %d" % args.num_sample) 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) amp_autocast = contextlib.nullcontext() loss_scaler = "none" 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) if not args.no_amp: print(f"Use bf16: {args.bf16}") dtype = torch.bfloat16 if args.bf16 else torch.float16 amp_autocast = torch.cuda.amp.autocast(dtype=dtype) 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, start_warmup_value=args.warmup_lr, 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) 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: preds_file = os.path.join(args.output_dir, str(global_rank) + '.txt') test_stats = final_test( data_loader_test, model, device, preds_file, amp_autocast, ds=args.enable_deepspeed, no_amp=args.no_amp, bf16=args.bf16, maxk=5 if args.nb_classes >= 5 else 1 ) 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:.4f}%") # 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") 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, amp_autocast, 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, no_amp=args.no_amp, bf16=args.bf16 ) 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) utils.save_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, epoch=epoch, model_name='latest', model_ema=model_ema) if data_loader_val is not None: test_stats = validation_one_epoch( data_loader_val, model, device, amp_autocast, ds=args.enable_deepspeed, no_amp=args.no_amp, bf16=args.bf16, maxk=5 if args.nb_classes >= 5 else 1 ) timestep = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) print(f"[{timestep}] Accuracy of the network on the {len(dataset_val)} val videos: {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=epoch, model_name='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_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') if args.test_best: print("Auto testing the best model") args.eval = True utils.auto_load_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema) test_stats = final_test( data_loader_test, model, device, preds_file, amp_autocast, ds=args.enable_deepspeed, no_amp=args.no_amp, bf16=args.bf16, maxk=5 if args.nb_classes >= 5 else 1 ) torch.distributed.barrier() 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)