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 single_modality.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 from models import * from models.internvl_clip_vision import inflate_weight 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) parser.add_argument('--steps_per_print', default=1, type=int) parser.add_argument('--use_ceph_checkpoint', action='store_true', help="whether use ceph to save and load checkpoint, may be some bug now") parser.set_defaults(use_ceph_checkpoint=False) parser.add_argument('--ceph_checkpoint_prefix', default='', type=str, help='prefix for checkpoint in ceph') parser.add_argument('--ckpt_path_split', default='/exp/', type=str, help='string for splitting the ckpt_path') # 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='videos input size') parser.add_argument('--layer_scale_init_value', default=1e-5, type=float, help="0.1 for base, 1e-5 for large. set 0 to disable LayerScale") parser.add_argument('--layerscale_no_force_fp32', action='store_true', help="Not force fp32 for LayerScale") parser.set_defaults(layerscale_no_force_fp32=False) parser.add_argument('--sep_pos_embed', action='store_true', help="whether use seperable position embedding") parser.add_argument('--center_init', action='store_true', help="center initlization for patch embedding") 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('--head_drop_path', type=float, default=0.0, metavar='PCT', help='Head Drop path rate (default: 0.0)') 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-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=float, 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('--data_set', default='Kinetics', choices=[ 'Kinetics', 'Kinetics_sparse', 'SSV2', 'UCF101', 'HMDB51', 'image_folder', 'mitv1_sparse', 'ANet', 'HACS', 'ANet_interval', 'HACS_interval', ], 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) # 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') 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'") 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_internvideo2_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, pretrained=False, num_classes=args.nb_classes, num_frames=args.num_frames * args.num_segments, tubelet_size=args.tubelet_size, sep_pos_embed=args.sep_pos_embed, fc_drop_rate=args.fc_drop_rate, drop_path_rate=args.drop_path, head_drop_path_rate=args.head_drop_path, use_checkpoint=args.use_checkpoint, checkpoint_num=args.checkpoint_num, init_scale=args.init_scale, init_values=args.layer_scale_init_value, layerscale_no_force_fp32=args.layerscale_no_force_fp32, ) 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 if checkpoint_model['patch_embed.proj.weight'].shape[2] == 1 and model.patch_embed.tubelet_size > 1: print("Inflate patch embedding") print(f"Use center initilization: {args.center_init}") checkpoint_model['patch_embed.proj.weight'] = inflate_weight( checkpoint_model['patch_embed.proj.weight'][:, :, 0], model.patch_embed.tubelet_size, center=args.center_init ) # 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 # we use 8 frames for pretraining orig_t_size = 8 new_t_size = args.num_frames * args.num_segments // 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) # class_token and dist_token are kept unchanged if orig_t_size != new_t_size: print(f"Temporal interpolate from {orig_t_size} to {new_t_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， T, HW, C -> BHW, C, T (B = 1) pos_tokens = pos_tokens.view(1, orig_t_size, -1, embedding_size) pos_tokens = pos_tokens.permute(0, 2, 3, 1).reshape(-1, embedding_size, orig_t_size) pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=new_t_size, mode='linear') pos_tokens = pos_tokens.view(1, -1, embedding_size, new_t_size) pos_tokens = pos_tokens.permute(0, 3, 1, 2).reshape(1, -1, embedding_size) new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) checkpoint_model['pos_embed'] = new_pos_embed pos_embed_checkpoint = new_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 elif 'pos_embed_spatial' in checkpoint_model and 'pos_embed_temporal' in checkpoint_model: pos_embed_spatial_checkpoint = checkpoint_model['pos_embed_spatial'] pos_embed_temporal_checkpoint = checkpoint_model['pos_embed_temporal'] embedding_size = pos_embed_spatial_checkpoint.shape[-1] # channel dim num_patches = model.patch_embed.num_patches # orig_t_size = pos_embed_temporal_checkpoint.shape[-2] new_t_size = args.num_frames // model.patch_embed.tubelet_size # height (== width) for the checkpoint position embedding orig_size = int(pos_embed_spatial_checkpoint.shape[-2] ** 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_temporal_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_temporal'] = tmp_pos_embed if orig_size != new_size: print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) pos_tokens = pos_embed_spatial_checkpoint # 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 checkpoint_model['pos_embed_spatial'] = pos_tokens 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) 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, 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) 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)) ceph_args = { 'use_ceph_checkpoint': args.use_ceph_checkpoint, 'ceph_checkpoint_prefix': args.ceph_checkpoint_prefix, 'ckpt_path_split': args.ckpt_path_split, 'local_rank': args.gpu, } if ceph_args['use_ceph_checkpoint']: print("Will automatically upload model on ceph") assert ceph_args['ceph_checkpoint_prefix'] != '', "Should set prefix for ceph checkpoint!" utils.auto_load_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema, ceph_args=ceph_args, ) print(f"Use bf16 {args.bf16}") 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, ds=args.enable_deepspeed, bf16=args.bf16) 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, 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, # ceph_args=ceph_args, # ) 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, ceph_args=ceph_args, ) if data_loader_val is not None: test_stats = validation_one_epoch(data_loader_val, model, device, ds=args.enable_deepspeed, bf16=args.bf16) 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, ceph_args=ceph_args, ) 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') 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, ceph_args=ceph_args, ) test_stats = final_test(data_loader_test, model, device, preds_file, ds=args.enable_deepspeed, bf16=args.bf16) 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") 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)