''' Evalute the linear probe performance on different checkpoints ''' import logging import os import random from datetime import datetime import copy import numpy as np import torch import torch.backends.cudnn as cudnn from torch.cuda.amp import GradScaler import glob try: import wandb except ImportError: wandb = None try: import torch.utils.tensorboard as tensorboard except ImportError: tensorboard = None try: import horovod.torch as hvd except ImportError: hvd = None from clap_module import create_model_and_transforms, trace_model, create_model from training.data import get_data from training.params import parse_args from training.distributed import is_master, init_distributed_device, world_info_from_env from training.logger import setup_logging from training.scheduler import cosine_lr from training.lp_main import config_lp_optimizer from training.lp_train import train_one_epoch, evaluate from clap_module.utils import get_tar_path_from_dataset_name, dataset_split from clap_module.utils import load_p, load_class_label from clap_module.linear_probe import LinearProbe def maintain_ckpts(args, startidx, all_idx_len): for i in reversed(range(startidx, all_idx_len)): if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")): os.rename( os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"), os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"), ) if os.path.exists( os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt") ): os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")) return def update_top_k_performance( new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True, pretrain_epoch=0 ): """ Record the top-k performance of the current epoch. current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...} """ if isinstance(new_metrics_inputs, (list, tuple)): new_metrics_inputs = np.mean(new_metrics_inputs) return update_top_k_performance( new_metrics_inputs, current_top_k_ckpt_metrics, args=args, ckpt=ckpt, bignumbetter=bignumbetter, pretrain_epoch=pretrain_epoch ) elif isinstance(new_metrics_inputs, dict): new_metrics_inputs = np.mean(list(new_metrics_inputs.values())) return update_top_k_performance( new_metrics_inputs, current_top_k_ckpt_metrics, args=args, ckpt=ckpt, bignumbetter=bignumbetter, pretrain_epoch=pretrain_epoch ) elif isinstance(new_metrics_inputs, (float, int)): update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()} sorted_keys = sorted(current_top_k_ckpt_metrics.keys()) sorted_values = sorted( current_top_k_ckpt_metrics.values(), reverse=bignumbetter ) sorted_values_ = copy.deepcopy(sorted_values) sorted_values.append(new_metrics_inputs) sorted_values = sorted(sorted_values, reverse=bignumbetter) sorted_values = sorted_values[:-1] if sorted_values == sorted_values_: return current_top_k_ckpt_metrics, new_metrics_inputs else: for i in range(len(sorted_keys)): if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]: current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i] update_flag[sorted_keys[i]] = True for i in range(len(update_flag)): if update_flag[i]: maintain_ckpts(args, i, len(sorted_keys)) torch.save( ckpt, os.path.join(args.checkpoint_path, f"pretrain_epoch_{pretrain_epoch}_lp_epoch_top_{i}.pt"), ) break return current_top_k_ckpt_metrics, new_metrics_inputs # def updateifNone(a, b): # a = b if None else a # return a def is_pretrained_params(n): return ( n.startswith("clap_model.transformer") or n in ["clap_model.positional_embedding", "clap_model.text_projection"] or n.startswith("clap_model.token_embedding") or n.startswith("clap_model.ln_final") or n.startswith("clap_model.logit_scale_t") ) def random_seed(seed=42, rank=0): torch.manual_seed(seed + rank) np.random.seed(seed + rank) random.seed(seed + rank) def main(): args = parse_args() # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule? args.amodel = args.amodel.replace("/", "-") pretrained_ckpts = sorted(glob.glob(os.path.join(args.pretrained, "*.pt")), key=os.path.getmtime) if args.name is None: args.name = "-".join( [ datetime.now().strftime("%Y_%m_%d-%H_%M_%S"), f"linear_probe" f"model_{args.amodel}", f"lr_{args.lr}", f"b_{args.batch_size}", f"j_{args.workers}", f"p_{args.precision}", ] ) # discover initial world args early so we can log properly args.distributed = False args.local_rank, args.rank, args.world_size = world_info_from_env() if args.remotedata and is_master(args): for dataset_name in args.datasetnames: for split in dataset_split[dataset_name]: if not os.path.exists(f"./json_files/{dataset_name}/{split}"): os.makedirs(f"./json_files/{dataset_name}/{split}") os.system( f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json" ) args.log_path = None if is_master(args, local=args.log_local): log_base_path = os.path.join(args.logs, args.name) os.makedirs(log_base_path, exist_ok=True) log_filename = f"out-{args.rank}" if args.log_local else "out.log" args.log_path = os.path.join(log_base_path, log_filename) # avoid log dir in same name: postfix = 0 while os.path.exists(args.log_path): postfix += 1 log_base_path_new = log_base_path+'-'+str(postfix) os.makedirs(log_base_path_new, exist_ok=True) log_filename = f"out-{args.rank}" if args.log_local else "out.log" args.log_path = os.path.join(log_base_path_new, log_filename) # print( # "Error. Experiment already exists. Use --name {} to specify a new experiment." # ) # return -1 # Set logger args.log_level = logging.DEBUG if args.debug else logging.INFO setup_logging(args.log_path, args.log_level) # fully initialize distributed device environment device = init_distributed_device(args) args.wandb = "wandb" in args.report_to or "all" in args.report_to args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to if is_master(args): args.tensorboard_path = ( os.path.join(args.logs, args.name, "tensorboard") if args.tensorboard else "" ) args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints") for dirname in [args.tensorboard_path, args.checkpoint_path]: if dirname: os.makedirs(dirname, exist_ok=True) else: args.tensorboard_path = "" args.checkpoint_path = "" if args.copy_codebase: copy_codebase(args) assert args.precision in ["amp", "fp16", "fp32"] if args.precision == "fp16": logging.warning( "It is recommended to use AMP mixed-precision instead of FP16. " "FP16 support needs further verification and tuning, especially for train." ) if args.horovod: logging.info( f"Running in horovod mode with multiple processes / nodes. Device: {args.device}." f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}." ) elif args.distributed: logging.info( f"Running in distributed mode with multiple processes. Device: {args.device}." f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}." ) else: logging.info(f"Running with a single process. Device {args.device}.") logging.info(f'openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}') # determine if this worker should save logs and checkpoints. only do so if it is rank == 0 args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args) writer = None if args.save_logs and args.tensorboard: assert tensorboard is not None, "Please install tensorboard." writer = tensorboard.SummaryWriter(args.tensorboard_path) if args.wandb and is_master(args): assert wandb is not None, "Please install wandb." logging.debug("Starting wandb.") # you will have to configure this for your project! wandb.init( project="clap", notes=args.wandb_notes, name=args.wandb_notes, tags=[], config=vars(args), ) logging.debug("Finished loading wandb.") for idx, f in enumerate(pretrained_ckpts): logging.info(f"pretrained on {f}") args.pretrained = f ckpt = torch.load(f, map_location='cpu') pretrain_epoch = 0 if 'epoch' in ckpt: pretrain_epoch = ckpt['epoch'] # train best_metrics = lp_main(args, device, writer, pretrain_epoch, idx) if args.wandb and is_master(args): assert wandb is not None, "Please install wandb." for name, val in best_metrics.items(): wandb.log({f"val/summary/{name}": val, "epoch": pretrain_epoch}) if args.wandb and is_master(args): wandb.finish() def update_metric(best_metric, new_metric): for key in new_metric: if key not in best_metric: best_metric[key] = new_metric[key] else: best_metric[key] = max(best_metric[key], new_metric[key]) return best_metric def lp_main(args, device, writer, pretrain_epoch, idx): random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) np.random.seed(args.seed) args.class_index_dict = load_class_label(args.class_label_path) # Create CLAP model clap_model, clap_model_cfg = create_model( args.amodel, args.tmodel, args.pretrained, precision=args.precision, device=device, jit=args.torchscript, force_quick_gelu=args.force_quick_gelu, openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir), skip_params=False, enable_fusion=args.enable_fusion, fusion_type=args.fusion_type ) args.lp_out_ch = len(list(args.class_index_dict.keys())) # Linear Probe if idx == 0: logging.info(f"linear probe using mlp: {args.lp_mlp}") logging.info(f"linear probe using freeze: {args.lp_freeze}") logging.info(f"linear probe act layer: {args.lp_act}") logging.info(f"linear probe out ch: {args.lp_out_ch}") logging.info(f"linear probe learning rate (if applicable): {args.lp_lr}") logging.info(f"linear probe loss func: {args.lp_loss}") logging.info(f"linear probe lp_metrics: {args.lp_metrics}") model = LinearProbe( clap_model, mlp=args.lp_mlp, freeze=args.lp_freeze, in_ch=512, out_ch=args.lp_out_ch, act=args.lp_act ) # in_ch is fixed (i.e., 512) model = model.to(device) if args.horovod: with torch.no_grad(): for param in model.parameters(): param.set_(param.contiguous()) if args.trace: model = trace_model(model, batch_size=args.batch_size, device=device) if is_master(args) and idx == 0: logging.info("Linear Probe CLAP Model:") logging.info(f"{str(clap_model)}") logging.info("Params:") params_file = os.path.join(args.logs, args.name, "params.txt") with open(params_file, "w") as f: for name in sorted(vars(args)): val = getattr(args, name) logging.info(f" {name}: {val}") f.write(f"{name}: {val}\n") if args.distributed and not args.horovod: if args.use_bn_sync: model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) ddp_args = {} if args.ddp_static_graph: # this doesn't exist in older PyTorch, arg only added if enabled ddp_args["static_graph"] = True model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[device], find_unused_parameters=True, **ddp_args ) data = get_data(args, clap_model_cfg) assert len(data), "At least one train or eval dataset must be specified." if args.trace: assert "train" not in data, "Cannot train with traced model" optimizer, scheduler, text_freeze_parameters = config_lp_optimizer(model, data, args) scaler = GradScaler() if args.precision == "amp" else None # optionally resume from a checkpoint start_epoch = 0 if args.resume is not None: if os.path.isfile(args.resume): checkpoint = torch.load(args.resume, map_location=device) if "epoch" in checkpoint: # resuming a train checkpoint w/ epoch and optimizer state start_epoch = checkpoint["epoch"] sd = checkpoint["state_dict"] if not args.distributed and next(iter(sd.items()))[0].startswith( "module" ): sd = {k[len("module.") :]: v for k, v in sd.items()} model.load_state_dict(sd) if args.split_opt: if optimizer is not None: for k, o_ in optimizer.items(): o_.load_state_dict(checkpoint[k + "_" + "optimizer"]) if optimizer is not None: optimizer.load_state_dict(checkpoint["optimizer"]) if scaler is not None and "scaler" in checkpoint: scaler.load_state_dict(checkpoint["scaler"]) logging.info( f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})" ) else: # loading a bare (model only) checkpoint for fine-tune or evaluation model.load_state_dict(checkpoint) logging.info( f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})" ) if args.freeze_text: print("Freeze Text!!!!") for k in text_freeze_parameters: k.requires_grad = False else: logging.info("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True cudnn.deterministic = False if args.wandb and is_master(args): args.train_sz = data["train"].dataloader.num_samples if args.val_data is not None: args.val_sz = data["val"].dataloader.num_samples if args.debug: wandb.watch(model, log="all") if idx == 0: wandb.save(params_file) best_metrics = {} if "train" not in data: metric = evaluate(model, data, start_epoch, args, writer, extra_suffix="_pe@" + str(pretrain_epoch)) if is_master(args): best_metrics = update_metric(best_metrics, metric) return elif start_epoch == 0 and "val" in data and not args.no_eval: metric = evaluate(model, data, 0, args, writer, extra_suffix="_pe@" + str(pretrain_epoch)) if is_master(args): best_metrics = update_metric(best_metrics, metric) if args.save_top_performance: current_top_k_ckpt_metrics = { i: 0 for i in range(args.save_top_performance) } # initialize the top-k metric for ckpts to 0 for epoch in range(start_epoch, args.epochs): # freeze the text param after (include) args.freeze_text_after, this is -1 by default if epoch == args.freeze_text_after: print("Text pretrained parameters are freezed since this epoch.") for k in text_freeze_parameters: k.requires_grad = False if is_master(args): logging.info(f"Start epoch {epoch}") train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer, extra_suffix="_pe@" + str(pretrain_epoch)) completed_epoch = epoch + 1 if any(v in data for v in ("val", "imagenet-val", "imagenet-v2")) and not args.no_eval: metric = evaluate(model, data, completed_epoch, args, writer, extra_suffix="_pe@" + str(pretrain_epoch)) if is_master(args): best_metrics = update_metric(best_metrics, metric) if args.save_top_performance: top_k_dataset = args.top_k_checkpoint_select_dataset top_k_metric = args.top_k_checkpoint_select_metric filtered_metrics = [ v for k, v in metric.items() if top_k_metric in k and top_k_dataset in k ] # check all R@10 metrics (all dataset) and use it to update the ckpt # Saving checkpoints. if args.save_logs: opt_dict = { k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items() } checkpoint_dict = { "epoch": completed_epoch, "pretrain_epoch": pretrain_epoch, "name": args.name, "state_dict": model.state_dict(), } checkpoint_dict.update(opt_dict) if scaler is not None: checkpoint_dict["scaler"] = scaler.state_dict() if completed_epoch == args.epochs or ( args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0 ): torch.save( checkpoint_dict, os.path.join(args.checkpoint_path, f"pretrain_epoch_{pretrain_epoch}_lp_epoch_{completed_epoch}.pt"), ) if args.save_most_recent: torch.save( checkpoint_dict, os.path.join(args.checkpoint_path, f"pretrain_epoch_{pretrain_epoch}_lp_epoch_latest.pt"), ) if args.save_top_performance and not args.no_eval: update_top_k_performance( filtered_metrics, current_top_k_ckpt_metrics, args, checkpoint_dict, bignumbetter=True, pretrain_epoch=pretrain_epoch ) del clap_model return best_metrics def copy_codebase(args): from shutil import copytree, ignore_patterns new_code_path = os.path.join(args.logs, args.name, "code") if os.path.exists(new_code_path): print( f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment." ) return -1 print(f"Copying codebase to {new_code_path}") current_code_path = os.path.realpath(__file__) for _ in range(3): current_code_path = os.path.dirname(current_code_path) copytree( current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb") ) print("Done copying code.") return 1 if __name__ == "__main__": main()