LAVT-RIS/donghwa/train_mosaic_retrieval_warmup.py

import datetime
import os
import time

import torch
import torch.utils.data
from torch import nn

import yaml
from functools import reduce
import operator
from bert.modeling_bert import BertModel

import torchvision
from lib import segmentation

import transforms as T
import utils
import numpy as np

import torch.nn.functional as F

import gc
from collections import OrderedDict
from data.utils import MosaicVisualization, COCOVisualization
from torch.utils.tensorboard import SummaryWriter
from data.utils import get_warmup_value

def get_dataset(image_set, transform, args):
    
    assert "retrieval" in args.model_id 
    if args.dataset == "grefcoco":
        from data.dataset_grefer_mosaic_retrieval import GReferDataset
        ds = GReferDataset(args=args, 
                           refer_root=args.refer_data_root, 
                           dataset_name=args.dataset,
                           splitby=args.splitBy,
                           split=image_set, 
                           image_root=os.path.join(args.refer_data_root, 'images/train2014')
                           )
    
    else :
        from data.dataset_refer_bert_mosaic_retrieval_warmup import ReferDataset
        ds = ReferDataset(args,
                        split=image_set
                        )
        
    if image_set == "val" :
        fpath = os.path.join('coco-data-vis-retrieval-filter', args.model_id, 'val')
        MosaicVisualization(ds, fpath)
    num_classes = 2

    return ds, num_classes


# IoU calculation for validation
def IoU(pred, gt):
    pred = pred.argmax(1)

    intersection = torch.sum(torch.mul(pred, gt))
    union = torch.sum(torch.add(pred, gt)) - intersection

    if intersection == 0 or union == 0:
        iou = 0
    else:
        iou = float(intersection) / float(union)

    return iou, intersection, union


def get_transform(args):
    transforms = [T.Resize(args.img_size, args.img_size),
                  T.ToTensor(),
                  T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
                  ]

    return T.Compose(transforms)


def criterion(input, target):
    weight = torch.FloatTensor([0.9, 1.1]).cuda()
    return nn.functional.cross_entropy(input, target, weight=weight)


def evaluate(model, data_loader, bert_model):
    model.eval()
    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Test:'
    total_its = 0
    acc_ious = 0

    # evaluation variables
    cum_I, cum_U = 0, 0
    eval_seg_iou_list = [.5, .6, .7, .8, .9]
    seg_correct = np.zeros(len(eval_seg_iou_list), dtype=np.int32)
    seg_total = 0
    mean_IoU = []

    with torch.no_grad():
        for data in metric_logger.log_every(data_loader, 100, header):
            total_its += 1
            image, target, sentences, attentions = data['image'], data['seg_target'], data['sentence'], data['attn_mask']
            image, target, sentences, attentions = image.cuda(non_blocking=True),\
                                                   target.cuda(non_blocking=True),\
                                                   sentences.cuda(non_blocking=True),\
                                                   attentions.cuda(non_blocking=True)

            sentences = sentences.squeeze(1)
            attentions = attentions.squeeze(1)

            if bert_model is not None:
                last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]
                embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
                attentions = attentions.unsqueeze(dim=-1)  # (B, N_l, 1)
                output = model(image, embedding, l_mask=attentions)
            else:
                output = model(image, sentences, l_mask=attentions)

            iou, I, U = IoU(output, target)
            acc_ious += iou
            mean_IoU.append(iou)
            cum_I += I
            cum_U += U
            for n_eval_iou in range(len(eval_seg_iou_list)):
                eval_seg_iou = eval_seg_iou_list[n_eval_iou]
                seg_correct[n_eval_iou] += (iou >= eval_seg_iou)
            seg_total += 1
        iou = acc_ious / total_its

    mean_IoU = np.array(mean_IoU)
    mIoU = np.mean(mean_IoU)
    print('Final results:')
    print('Mean IoU is %.2f\n' % (mIoU * 100.))
    results_str = ''
    precs = []
    for n_eval_iou in range(len(eval_seg_iou_list)):
        results_str += '    precision@%s = %.2f\n' % \
                       (str(eval_seg_iou_list[n_eval_iou]), seg_correct[n_eval_iou] * 100. / seg_total)
        precs.append(seg_correct[n_eval_iou] * 100. / seg_total)
    results_str += '    overall IoU = %.2f\n' % (cum_I * 100. / cum_U)
    print(results_str)

    return 100 * iou, 100 * cum_I / cum_U, precs


def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq,
                    iterations, bert_model):
    model.train()
    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))
    header = 'Epoch: [{}]'.format(epoch)
    train_loss = 0
    total_its = 0

    for data in metric_logger.log_every(data_loader, print_freq, header):
        total_its += 1
        image, target, sentences, attentions = data['image'], data['seg_target'], data['sentence'], data['attn_mask']
        image, target, sentences, attentions = image.cuda(non_blocking=True),\
                                               target.cuda(non_blocking=True),\
                                               sentences.cuda(non_blocking=True),\
                                               attentions.cuda(non_blocking=True)

        sentences = sentences.squeeze(1)
        attentions = attentions.squeeze(1)

        if bert_model is not None:
            last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]  # (6, 10, 768)
            embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
            attentions = attentions.unsqueeze(dim=-1)  # (batch, N_l, 1)
            output = model(image, embedding, l_mask=attentions)
        else:
            output = model(image, sentences, l_mask=attentions)

        loss = criterion(output, target)
        optimizer.zero_grad()  # set_to_none=True is only available in pytorch 1.6+
        loss.backward()
        optimizer.step()
        lr_scheduler.step()

        torch.cuda.synchronize()
        train_loss += loss.item()
        iterations += 1
        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])

        del image, target, sentences, attentions, loss, output, data
        if bert_model is not None:
            del last_hidden_states, embedding

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.synchronize()
        
    loss_log = {
        'loss': metric_logger.meters['loss'].global_avg
        }
    return iterations, loss_log


def main(args):
    writer = SummaryWriter('./experiments/{}'.format(args.model_id))

    dataset, num_classes = get_dataset("train",
                                       get_transform(args=args),
                                       args=args)
    dataset_test, _ = get_dataset("val",
                                  get_transform(args=args),
                                  args=args)

    # batch sampler
    print(f"local rank {args.local_rank} / global rank {utils.get_rank()} successfully built train dataset.")
    num_tasks = utils.get_world_size()
    global_rank = utils.get_rank()
    train_sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank,
                                                                    shuffle=True)
    test_sampler = torch.utils.data.SequentialSampler(dataset_test)

    # data loader
    data_loader = torch.utils.data.DataLoader(
        dataset, batch_size=args.batch_size, 
        sampler=train_sampler, num_workers=args.workers, pin_memory=args.pin_mem, drop_last=True)

    data_loader_test = torch.utils.data.DataLoader(
        dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers)
    
    # model initialization
    print(args.model)
    model = segmentation.__dict__[args.model](pretrained=args.pretrained_swin_weights,
                                              args=args)
    model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
    model.cuda()
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True)
    single_model = model.module

    if args.model != 'lavt_one':
        model_class = BertModel
        bert_model = model_class.from_pretrained(args.ck_bert)
        bert_model.pooler = None  # a work-around for a bug in Transformers = 3.0.2 that appears for DistributedDataParallel
        bert_model.cuda()
        bert_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(bert_model)
        bert_model = torch.nn.parallel.DistributedDataParallel(bert_model, device_ids=[args.local_rank])
        single_bert_model = bert_model.module
    else:
        bert_model = None
        single_bert_model = None

    # resume training
    if args.resume:
        checkpoint = torch.load(args.resume, map_location='cpu')
        single_model.load_state_dict(checkpoint['model'])
        if args.model != 'lavt_one':
            single_bert_model.load_state_dict(checkpoint['bert_model'])

    # parameters to optimize
    backbone_no_decay = list()
    backbone_decay = list()
    for name, m in single_model.backbone.named_parameters():
        if 'norm' in name or 'absolute_pos_embed' in name or 'relative_position_bias_table' in name:
            backbone_no_decay.append(m)
        else:
            backbone_decay.append(m)

    if args.model != 'lavt_one':
        params_to_optimize = [
            {'params': backbone_no_decay, 'weight_decay': 0.0},
            {'params': backbone_decay},
            {"params": [p for p in single_model.classifier.parameters() if p.requires_grad]},
            # the following are the parameters of bert
            {"params": reduce(operator.concat,
                              [[p for p in single_bert_model.encoder.layer[i].parameters()
                                if p.requires_grad] for i in range(10)])},
        ]
    else:
        params_to_optimize = [
            {'params': backbone_no_decay, 'weight_decay': 0.0},
            {'params': backbone_decay},
            {"params": [p for p in single_model.classifier.parameters() if p.requires_grad]},
            # the following are the parameters of bert
            {"params": reduce(operator.concat,
                              [[p for p in single_model.text_encoder.encoder.layer[i].parameters()
                                if p.requires_grad] for i in range(10)])},
        ]

    # optimizer
    optimizer = torch.optim.AdamW(params_to_optimize,
                                  lr=args.lr,
                                  weight_decay=args.weight_decay,
                                  amsgrad=args.amsgrad
                                  )

    # learning rate scheduler
    lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
                                                     lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)

    # housekeeping
    start_time = time.time()
    iterations = 0
    best_oIoU = -0.1

    # resume training (optimizer, lr scheduler, and the epoch)
    if args.resume:
        optimizer.load_state_dict(checkpoint['optimizer'])
        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
        resume_epoch = checkpoint['epoch']
    else:
        resume_epoch = -999

    # training loops
    for epoch in range(max(0, resume_epoch+1), args.epochs):
        dataset.epoch = epoch
        data_loader.sampler.set_epoch(epoch)
        if epoch%10 == 0:
            fpath = os.path.join('coco-data-vis-retrieval-filter', args.model_id, 'train', "epoch%02d"%epoch) 
            MosaicVisualization(dataset, fpath)
        itrs_temp, loss_log = train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq,
                        iterations, bert_model)
        iterations += itrs_temp 
        iou, overallIoU, precs = evaluate(model, data_loader_test, bert_model)

        print('Average object IoU {}'.format(iou))
        print('Overall IoU {}'.format(overallIoU))
        save_checkpoint = (best_oIoU < overallIoU)
        if save_checkpoint:
            print('Better epoch: {}\n'.format(epoch))
            if single_bert_model is not None:
                dict_to_save = {'model': single_model.state_dict(), 'bert_model': single_bert_model.state_dict(),
                                'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
                                'lr_scheduler': lr_scheduler.state_dict()}
            else:
                dict_to_save = {'model': single_model.state_dict(),
                                'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args,
                                'lr_scheduler': lr_scheduler.state_dict()}

            utils.save_on_master(dict_to_save, os.path.join(args.output_dir,
                                                            'model_best_{}.pth'.format(args.model_id)))
            best_oIoU = overallIoU
        
        # dataset_refer_bert_mosaic_retrieval_warmup.py
        aug_prob = args.aug.aug_prob # 0.6
        retr_prob = args.aug.retr_prob # 0.3
        rand_prob = aug_prob - retr_prob # 0.3
        
        if epoch < args.aug.warmup_epoch :
            retr_prob = get_warmup_value(0, retr_prob, epoch, args.aug.warmup_epoch)
            rand_prob = get_warmup_value(aug_prob, rand_prob, epoch, args.aug.warmup_epoch)
        
        # aug_prob = args.aug.aug_prob
        # if epoch < args.aug.retrieval_epoch:
        #     rand_prob = aug_prob
        #     retr_prob = 0
        # else :
        #     rand_prob = args.aug.rand_prob
        #     retr_prob = args.aug.retr_prob   
        
        if utils.is_main_process():
            writer.add_scalar('val/mIoU', iou, epoch)
            writer.add_scalar('val/oIoU', overallIoU, epoch)
            writer.add_scalar('val/Prec/50', precs[0], epoch)
            writer.add_scalar('val/Prec/60', precs[1], epoch)
            writer.add_scalar('val/Prec/70', precs[2], epoch)
            writer.add_scalar('val/Prec/80', precs[3], epoch)
            writer.add_scalar('val/Prec/90', precs[4], epoch)
            writer.add_scalar('train/loss', loss_log['loss'], epoch)
            writer.add_scalar('train/one_prob', 1-aug_prob, epoch)
            writer.add_scalar('train/retr_prob', retr_prob, epoch)
            writer.add_scalar('train/rand_prob', rand_prob, epoch)

    writer.flush()
    # summarize
    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__":
    from args import get_parser
    parser = get_parser()
    args = parser.parse_args()
    if args.config is not None :
        from config.utils import CfgNode
        cn = CfgNode(CfgNode.load_yaml_with_base(args.config))
        for k,v in cn.items():
            if not hasattr(args, k):
                print('Warning: key %s not in args' %k)
            setattr(args, k, v)
        args = parser.parse_args(namespace=args)
    print(args)
    args.output_dir = './experiments/{}/{}'.format("_".join([args.dataset, args.splitBy]), args.model_id)
    # set up distributed learning
    utils.init_distributed_mode(args)
    print('Image size: {}'.format(str(args.img_size)))
    main(args)