main_vqa.py

from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import json
import torch
import torch.nn.functional as F
from torch.utils.data import (SequentialSampler)
import numpy as np
import random
import os
import time
import argparse
from HBI.models.tokenization_clip import SimpleTokenizer as ClipTokenizer
from HBI.models.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from HBI.models.modeling_qa import HBI
from HBI.models.optimization import BertAdam
from torch.utils.data import DataLoader
from HBI.utils.util import parallel_apply, get_logger
from HBI.dataloaders.dataloader_msrvtt_qa import MSRVTT_DataLoader, MSRVTT_TrainDataLoader
from HBI.utils.metrics_qa import accuracy, AverageMeter
torch.distributed.init_process_group(backend="nccl")


global logger

def get_args(description='Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning'):
    parser = argparse.ArgumentParser(description=description)
    parser.add_argument("--do_pretrain", action='store_true', help="Whether to run training.")
    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
    parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.")

    parser.add_argument('--train_csv', type=str, default='data/.train.csv', help='')
    parser.add_argument('--val_csv', type=str, default='data/.val.csv', help='')
    parser.add_argument('--data_path', type=str, default='train_ans2label.json', help='data pickle file path')
    parser.add_argument('--features_path', type=str, default='MSRVTT_Videos', help='feature path')

    parser.add_argument('--num_thread_reader', type=int, default=1, help='')
    parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate')
    parser.add_argument('--epochs', type=int, default=20, help='upper epoch limit')
    parser.add_argument('--batch_size', type=int, default=256, help='batch size')
    parser.add_argument('--batch_size_val', type=int, default=3500, help='batch size eval')
    parser.add_argument('--lr_decay', type=float, default=0.9, help='Learning rate exp epoch decay')
    parser.add_argument('--n_display', type=int, default=100, help='Information display frequence')
    parser.add_argument('--video_dim', type=int, default=1024, help='video feature dimension')
    parser.add_argument('--seed', type=int, default=42, help='random seed')
    parser.add_argument('--max_words', type=int, default=20, help='')
    parser.add_argument('--max_frames', type=int, default=100, help='')
    parser.add_argument('--feature_framerate', type=int, default=1, help='')
    parser.add_argument('--margin', type=float, default=0.1, help='margin for loss')
    parser.add_argument('--hard_negative_rate', type=float, default=0.5, help='rate of intra negative sample')
    parser.add_argument('--negative_weighting', type=int, default=1, help='Weight the loss for intra negative')
    parser.add_argument('--n_pair', type=int, default=1, help='Num of pair to output from data loader')

    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")
    parser.add_argument("--cross_model", default="cross-base", type=str, required=False, help="Cross module")
    parser.add_argument("--init_model", default=None, type=str, required=False, help="Initial model.")
    parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.")
    parser.add_argument("--warmup_proportion", default=0.1, type=float,
                        help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% of training.")
    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument('--n_gpu', type=int, default=1, help="Changed in the execute process.")

    parser.add_argument("--cache_dir", default="", type=str,
                        help="Where do you want to store the pre-trained models downloaded from s3")

    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
    parser.add_argument('--fp16_opt_level', type=str, default='O1',
                        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
                             "See details at https://nvidia.github.io/apex/amp.html")

    parser.add_argument("--task_type", default="retrieval", type=str, help="Point the task `retrieval` to finetune.")
    parser.add_argument("--datatype", default="msrvtt", type=str, help="Point the dataset to finetune.")

    parser.add_argument("--world_size", default=0, type=int, help="distribted training")
    parser.add_argument("--local_rank", default=0, type=int, help="distribted training")
    parser.add_argument("--rank", default=0, type=int, help="distribted training")
    parser.add_argument('--coef_lr', type=float, default=0.001, help='coefficient for bert branch.')
    parser.add_argument('--use_mil', action='store_true', help="Whether use MIL as Miech et. al. (2020).")
    parser.add_argument('--sampled_use_mil', action='store_true', help="Whether MIL, has a high priority than use_mil.")

    parser.add_argument('--text_num_hidden_layers', type=int, default=12, help="Layer NO. of text.")
    parser.add_argument('--visual_num_hidden_layers', type=int, default=12, help="Layer NO. of visual.")
    parser.add_argument('--cross_num_hidden_layers', type=int, default=4, help="Layer NO. of cross.")

    parser.add_argument('--loose_type', action='store_true', help="Default using tight type for retrieval.")
    parser.add_argument('--expand_msrvtt_sentences', action='store_true', help="")

    parser.add_argument('--train_frame_order', type=int, default=0, choices=[0, 1, 2],
                        help="Frame order, 0: ordinary order; 1: reverse order; 2: random order.")
    parser.add_argument('--eval_frame_order', type=int, default=0, choices=[0, 1, 2],
                        help="Frame order, 0: ordinary order; 1: reverse order; 2: random order.")

    parser.add_argument('--freeze_layer_num', type=int, default=0, help="Layer NO. of CLIP need to freeze.")
    parser.add_argument('--slice_framepos', type=int, default=0, choices=[0, 1, 2],
                        help="0: cut from head frames; 1: cut from tail frames; 2: extract frames uniformly.")
    parser.add_argument('--linear_patch', type=str, default="2d", choices=["2d", "3d"],
                        help="linear projection of flattened patches.")
    parser.add_argument('--sim_header', type=str, default="meanP",
                        choices=["meanP", "seqLSTM", "seqTransf", "tightTransf", "BTransf", "denseTransf"],
                        help="choice a similarity header.")
    parser.add_argument('--loss', type=str, default="CrossEn",
                        choices=["CrossEn", "DualLoss", "CrossCLR", "MarginLoss", "DCWLoss"])
    parser.add_argument("--estimator", default="None", type=str, required=False, help=" Banzhaf Interaction Estimator.")

    parser.add_argument('--kl', type=float, default=0.2)
    parser.add_argument('--skl', type=float, default=1)
    parser.add_argument('--num_labels', type=int, default=1000)
    
    parser.add_argument('--rate', type=float, default=[0.4, 0.4, 0.2])
    
    args = parser.parse_args()

    if args.sim_header == "tightTransf":
        args.loose_type = False

    # Check paramenters
    if args.gradient_accumulation_steps < 1:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
            args.gradient_accumulation_steps))
    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    args.batch_size = int(args.batch_size / args.gradient_accumulation_steps)

    return args

def set_seed_logger(args):
    global logger
    # predefining random initial seeds
    random.seed(args.seed)
    os.environ['PYTHONHASHSEED'] = str(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)  # if you are using multi-GPU.
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True

    world_size = torch.distributed.get_world_size()
    torch.cuda.set_device(args.local_rank)
    args.world_size = world_size
    rank = torch.distributed.get_rank()
    args.rank = rank

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir, exist_ok=True)

    logger = get_logger(os.path.join(args.output_dir, "log.txt"))

    if args.local_rank == 0:
        logger.info("Effective parameters:")
        for key in sorted(args.__dict__):
            logger.info("  <<< {}: {}".format(key, args.__dict__[key]))

    return args

def init_device(args, local_rank):
    global logger

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu", local_rank)

    n_gpu = torch.cuda.device_count()
    logger.info("device: {} n_gpu: {}".format(device, n_gpu))
    args.n_gpu = n_gpu

    if args.batch_size % args.n_gpu != 0 or args.batch_size_val % args.n_gpu != 0:
        raise ValueError("Invalid batch_size/batch_size_val and n_gpu parameter: {}%{} and {}%{}, should be == 0".format(
            args.batch_size, args.n_gpu, args.batch_size_val, args.n_gpu))

    return device, n_gpu

def init_model(args, device, n_gpu, local_rank):

    if args.init_model:
        model_state_dict = torch.load(args.init_model, map_location='cpu')
    else:
        model_state_dict = None

    # Prepare model
    cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed')
    model = HBI.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args)

    model.to(device)

    return model

def prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, local_rank, coef_lr=1.):

    if hasattr(model, 'module'):
        model = model.module

    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']

    decay_param_tp = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_decay)]
    no_decay_param_tp = [(n, p) for n, p in param_optimizer if any(nd in n for nd in no_decay)]

    decay_clip_param_tp = [(n, p) for n, p in decay_param_tp if "clip." in n]
    decay_noclip_param_tp = [(n, p) for n, p in decay_param_tp if "clip." not in n]

    no_decay_clip_param_tp = [(n, p) for n, p in no_decay_param_tp if "clip." in n]
    no_decay_noclip_param_tp = [(n, p) for n, p in no_decay_param_tp if "clip." not in n]

    weight_decay = 0.2
    optimizer_grouped_parameters = [
        {'params': [p for n, p in decay_clip_param_tp], 'weight_decay': weight_decay, 'lr': args.lr * coef_lr},
        {'params': [p for n, p in decay_noclip_param_tp], 'weight_decay': weight_decay},
        {'params': [p for n, p in no_decay_clip_param_tp], 'weight_decay': 0.0, 'lr': args.lr * coef_lr},
        {'params': [p for n, p in no_decay_noclip_param_tp], 'weight_decay': 0.0}
    ]

    scheduler = None
    optimizer = BertAdam(optimizer_grouped_parameters, lr=args.lr, warmup=args.warmup_proportion,
                         schedule='warmup_cosine', b1=0.9, b2=0.98, e=1e-6,
                         t_total=num_train_optimization_steps, weight_decay=weight_decay,
                         max_grad_norm=1.0)

    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank],
                                                      output_device=local_rank, find_unused_parameters=True)

    return optimizer, scheduler, model

def dataloader_msrvtt_train(args, tokenizer):
    msrvtt_dataset = MSRVTT_TrainDataLoader(
        jsonl_path=args.train_csv,
        ans2label_path=args.data_path,
        features_path=args.features_path,
        max_words=args.max_words,
        feature_framerate=args.feature_framerate,
        tokenizer=tokenizer,
        max_frames=args.max_frames,
        unfold_sentences=args.expand_msrvtt_sentences,
        frame_order=args.train_frame_order,
        slice_framepos=args.slice_framepos,
        use_num=args.num_labels,
    )

    train_sampler = torch.utils.data.distributed.DistributedSampler(msrvtt_dataset)
    dataloader = DataLoader(
        msrvtt_dataset,
        batch_size=args.batch_size // args.n_gpu,
        num_workers=args.num_thread_reader,
        pin_memory=True,
        shuffle=(train_sampler is None),
        sampler=train_sampler,
        drop_last=True,
    )

    return dataloader, len(msrvtt_dataset), train_sampler

def dataloader_msrvtt_test(args, tokenizer):
    msrvtt_testset = MSRVTT_DataLoader(
        jsonl_path=args.val_csv,
        train_jsonl=args.train_csv,
        ans2label_path=args.data_path,
        features_path=args.features_path,
        max_words=args.max_words,
        feature_framerate=args.feature_framerate,
        tokenizer=tokenizer,
        max_frames=args.max_frames,
        unfold_sentences=args.expand_msrvtt_sentences,
        frame_order=args.train_frame_order,
        slice_framepos=args.slice_framepos,
        use_num=args.num_labels,
    )
    dataloader_msrvtt = DataLoader(
        msrvtt_testset,
        batch_size=args.batch_size_val,
        num_workers=args.num_thread_reader,
        shuffle=False,
        drop_last=False,
    )
    return dataloader_msrvtt, len(msrvtt_testset)


def save_model(epoch, args, model, type_name=""):
    # Only save the model it-self
    model_to_save = model.module if hasattr(model, 'module') else model
    output_model_file = os.path.join(
        args.output_dir, "pytorch_model.bin.{}{}".format("" if type_name=="" else type_name+".", epoch))
    torch.save(model_to_save.state_dict(), output_model_file)
    logger.info("Model saved to %s", output_model_file)
    return output_model_file

def load_model(epoch, args, n_gpu, device, model_file=None):
    if model_file is None or len(model_file) == 0:
        model_file = os.path.join(args.output_dir, "pytorch_model.bin.{}".format(epoch))
    if os.path.exists(model_file):
        model_state_dict = torch.load(model_file, map_location='cpu')
        if args.local_rank == 0:
            logger.info("Model loaded from %s", model_file)
        # Prepare model
        cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed')
        model = EMCL4QA.from_pretrained(args.cross_model, cache_dir=cache_dir, state_dict=model_state_dict, task_config=args)

        model.to(device)
    else:
        model = None
    return model


def train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer, scheduler, global_step, local_rank=0, tokenizer=ClipTokenizer()):
    global logger
    torch.cuda.empty_cache()
    model.train()
    log_step = args.n_display
    start_time = time.time()
    total_loss = 0

    # train_dataloader = list(train_dataloader)

    for step, batch in enumerate(train_dataloader):
        if n_gpu == 1:
            # multi-gpu does scattering it-self
            batch = tuple(t.to(device=device, non_blocking=True) for t in batch)

        input_ids, input_mask, segment_ids, video, video_mask, labels = batch

        ce_loss = model(input_ids, segment_ids, input_mask, video, video_mask, labels)

        if n_gpu > 1:
            ce_loss = ce_loss.mean()

        if args.gradient_accumulation_steps > 1:
            ce_loss = ce_loss / args.gradient_accumulation_steps

        loss = ce_loss
        loss.backward()

        total_loss += float(loss)

        if (step + 1) % args.gradient_accumulation_steps == 0:

            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)

            if scheduler is not None:
                scheduler.step()  # Update learning rate schedule

            optimizer.step()
            optimizer.zero_grad()

            if hasattr(model, 'module'):
                torch.clamp_(model.module.clip.logit_scale.data, max=np.log(100))
            else:
                torch.clamp_(model.clip.logit_scale.data, max=np.log(100))

            global_step += 1
            if global_step % log_step == 0 and local_rank == 0:
                logger.info("Epoch: %d/%s, Step: %d/%d, Lr: %s, CeLoss: %f, Time/step: %f", epoch + 1,
                            args.epochs, step + 1,
                            len(train_dataloader), "-".join([str('%.9f'%itm) for itm in sorted(list(set(optimizer.get_lr())))]),
                            float(ce_loss),
                            (time.time() - start_time) / (log_step * args.gradient_accumulation_steps))
                start_time = time.time()

    total_loss = total_loss / len(train_dataloader)
    
    return total_loss, global_step

def eval_epoch(args, model, test_dataloader, device, n_gpu):
    top1 = AverageMeter()
    top5 = AverageMeter()

    if hasattr(model, 'module'):
        model = model.module.to(device)
    else:
        model = model.to(device)

    model.eval()
    with torch.no_grad():
        for bid, batch in enumerate(test_dataloader):
            batch = tuple(t.to(device) for t in batch)
            input_ids, input_mask, segment_ids, video, video_mask, labels = batch
            output = model(input_ids, segment_ids, input_mask, video, video_mask, labels)

            # measure accuracy and record loss
            prec1, prec5 = accuracy(output, labels, topk=(1, 5))
            top1.update(prec1[0], input_ids.size(0))
            top5.update(prec5[0], input_ids.size(0))

            print("{}/{}\r".format(bid, len(test_dataloader)), end="")

    logger.info("Video QA:")
    logger.info('\t>>> Prec@1: {top1.avg:.3f} - Prec@5: {top5.avg:.3f}'.
                format(top1=top1, top5=top5))

    R1 = top1.avg
    return R1

DATALOADER_DICT = {}
DATALOADER_DICT["msrvtt"] = {"train":dataloader_msrvtt_train, "val":None, "test":dataloader_msrvtt_test}

def main():
    global logger
    args = get_args()
    args = set_seed_logger(args)
    device, n_gpu = init_device(args, args.local_rank)

    tokenizer = ClipTokenizer()

    assert args.task_type == "retrieval"
    args.num_labels = 1500
    model = init_model(args, device, n_gpu, args.local_rank)

    ## ####################################
    # freeze testing
    ## ####################################
    assert args.freeze_layer_num <= 12 and args.freeze_layer_num >= -1
    if hasattr(model, "clip") and args.freeze_layer_num > -1:
        for name, param in model.clip.named_parameters():
            # top layers always need to train
            if name.find("ln_final.") == 0 or name.find("text_projection") == 0 or name.find("logit_scale") == 0 \
                    or name.find("visual.ln_post.") == 0 or name.find("visual.proj") == 0:
                continue    # need to train
            elif name.find("visual.transformer.resblocks.") == 0 or name.find("transformer.resblocks.") == 0:
                layer_num = int(name.split(".resblocks.")[1].split(".")[0])
                if layer_num >= args.freeze_layer_num:
                    continue    # need to train

            if args.linear_patch == "3d" and name.find("conv2."):
                continue
            else:
                # paramenters which < freeze_layer_num will be freezed
                param.requires_grad = False
    
    assert args.datatype in DATALOADER_DICT
    test_dataloader, test_length = DATALOADER_DICT[args.datatype]["test"](args, tokenizer)

    if DATALOADER_DICT[args.datatype]["val"] is not None:
        val_dataloader, val_length = DATALOADER_DICT[args.datatype]["val"](args, tokenizer, subset="val")
    else:
        val_dataloader, val_length = test_dataloader, test_length
        
    if args.local_rank == 0:
        logger.info("***** Running test *****")
        logger.info("  Num examples = %d", test_length)
        logger.info("  Batch size = %d", args.batch_size_val)
        logger.info("  Num steps = %d", len(test_dataloader))
        logger.info("***** Running val *****")
        logger.info("  Num examples = %d", val_length)

    if args.do_train:
        train_dataloader, train_length, train_sampler = DATALOADER_DICT[args.datatype]["train"](args, tokenizer)
        num_train_optimization_steps = (int(len(train_dataloader) + args.gradient_accumulation_steps - 1)
                                        / args.gradient_accumulation_steps) * args.epochs

        coef_lr = args.coef_lr
        optimizer, scheduler, model = prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, args.local_rank, coef_lr=coef_lr)

        if args.local_rank == 0:
            logger.info("***** Running training *****")
            logger.info("  Num examples = %d", train_length)
            logger.info("  Batch size = %d", args.batch_size)
            logger.info("  Num steps = %d", num_train_optimization_steps * args.gradient_accumulation_steps)

        best_score = 0.00001
        best_output_model_file = "None"
        global_step = 0
        for epoch in range(args.epochs):
            train_sampler.set_epoch(epoch)
            tr_loss, global_step = train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer,
                                               scheduler, global_step, local_rank=args.local_rank, tokenizer=tokenizer)
            if args.local_rank == 0:
                logger.info("Epoch %d/%s Finished, Train Loss: %f", epoch + 1, args.epochs, tr_loss)

                output_model_file = save_model(epoch, args, model, type_name="")
                logger.info("Eval on val dataset")
                R1 = eval_epoch(args, model, val_dataloader, device, n_gpu)
                if best_score <= R1:
                    best_score = R1
                    best_output_model_file = output_model_file
                logger.info("The best model is: {}, the Top1 Acc is: {:.4f}".format(best_output_model_file, best_score))

        ## Uncomment if want to test on the best checkpoint
        if args.local_rank == 0:
            model = load_model(-1, args, n_gpu, device, model_file=best_output_model_file)
            eval_epoch(args, model, test_dataloader, device, n_gpu)

    elif args.do_eval:
        if args.local_rank == 0:
            eval_epoch(args, model, test_dataloader, device, n_gpu)

if __name__ == "__main__":
    main()