RIS-DMMI/train_rev_refzom.py

import datetime
import os
import time

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

from functools import reduce
import operator
from bert.modeling_bert import BertModel
import json
from lib import segmentation
import pdb
import transforms 
from transforms import transform
from data.dataset_zom import Refzom_DistributedSampler,Referzom_Dataset
from data.dataset_zom_rev import Refzom_DistributedSampler, Referzom_Dataset_HP
from data.dataset_rev import ReferDataset_HP
import utils
import numpy as np
from torch.utils.tensorboard import SummaryWriter
import gc



def get_dataset(image_set, transform, args, eval_mode):
    if args.dataset == 'ref-zom':
        ds = Referzom_Dataset_HP(args,
                    split=image_set,
                    image_transforms=transform,
                    target_transforms=None,
                    eval_mode=eval_mode
                    )
    else:
        ds = ReferDataset_HP(args,
                        split=image_set,
                        image_transforms=transform,
                        target_transforms=None,
                        eval_mode=eval_mode
                        )
    num_classes = 2

    return ds, num_classes



def computeIoU(pred_seg, gd_seg):
    I = np.sum(np.logical_and(pred_seg, gd_seg))
    U = np.sum(np.logical_or(pred_seg, gd_seg))

    return I, U



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

    return transforms.Compose(transform)


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



def return_mask(emb_distance, verb_mask=None):
    B_, B_ = emb_distance.shape
    positive_mask = torch.zeros_like(emb_distance)
    positive_mask.fill_diagonal_(1)  # Set diagonal elements to 1 for all cases
    
    if B_ < len(verb_mask):
        # If B_ equals to 2*K (double the number of verb phrase)
        for i in range(B_ // 2):
            positive_mask[2 * i, 2 * i + 1] = 1
            positive_mask[2 * i + 1, 2 * i] = 1
    else:
        # Process the case where we have a mix of sentences with and without verbs
        i = 0
        while i < B_:
            if verb_mask[i] == 1:
                positive_mask[i, i + 1] = 1
                positive_mask[i + 1, i] = 1
                i += 2
            else:
                i += 1  
    negative_mask = torch.ones_like(emb_distance) - positive_mask
    return positive_mask, negative_mask


def UniAngularContrastLoss(total_fq, verb_mask, alpha=0.5, verbonly=True, m=0.5, tau=0.05, args=None):
    _, C, H, W = total_fq.shape
    
    if verbonly :
        B = total_fq[verb_mask].shape[0]
        emb = torch.mean(total_fq[verb_mask], dim=(-1, -2)).reshape(B, C)
        assert emb.shape[0] % 2 == 0, f"Embedding count {emb.shape[0]} is not divisible by 2."
    else :
        emb = torch.mean(total_fq, dim=-1)

    B_ = emb.shape[0]
    emb_i = emb.unsqueeze(1).repeat(1, B_, 1) # (B_, B_, C) 
    emb_j = emb.unsqueeze(0).repeat(B_, 1, 1) # (B_, B_, C)
    sim = nn.CosineSimilarity(dim=-1, eps=1e-6)
    sim_matrix = sim(emb_i, emb_j).reshape(B_, B_)  # (B_, B_)
    sim_matrix = torch.clamp(sim_matrix, min=-0.9999, max=0.9999)
    
    positive_mask, negative_mask = return_mask(sim_matrix, verb_mask)
    if len(positive_mask) > 0 : 
        sim_matrix_with_margin = sim_matrix.clone()
        sim_matrix_with_margin[positive_mask.bool()] = torch.cos(torch.acos(sim_matrix[positive_mask.bool()]) + m / 57.2958)        

        logits = sim_matrix_with_margin / tau
        exp_logits = torch.exp(logits)
        pos_exp_logits = exp_logits * positive_mask.long()
        pos_exp_logits = pos_exp_logits.sum(dim=-1)

        # print("pos_exp_logits: ", pos_exp_logits.shape)
        total_exp_logits = exp_logits.sum(dim=-1)
        positive_loss = -torch.log(pos_exp_logits / total_exp_logits)
        angular_loss = positive_loss.mean()

        return angular_loss
    else :
        return torch.tensor(0.0, device=total_fq.device)


    
def UniAngularLogitContrastLoss(total_fq, verb_mask, alpha=0.5, verbonly=True, m=0.5, tau=0.05, args=None):        
    epsilon = 1e-10  # Stability term for numerical issues
    _, C, H, W = total_fq.shape

    # Calculate embeddings
    if verbonly :
        B = total_fq[verb_mask].shape[0]
        emb = torch.mean(total_fq[verb_mask], dim=(-1, -2)).reshape(B, C)
        assert emb.shape[0] % 2 == 0, f"Embedding count {emb.shape[0]} is not divisible by 2."
    else :
        emb = torch.mean(total_fq, dim=-1)

    B_ = emb.shape[0]
    emb_i = emb.unsqueeze(1).repeat(1, B_, 1)  # (B_, B_, C)
    emb_j = emb.unsqueeze(0).repeat(B_, 1, 1)  # (B_, B_, C)

    sim = nn.CosineSimilarity(dim=-1, eps=1e-6)
    sim_matrix = sim(emb_i, emb_j).reshape(B_, B_)  # (B_, B_)
    sim_matrix = torch.clamp(sim_matrix, min=-0.9999, max=0.9999)

    margin_in_radians = m / 57.2958  # Convert degrees to radians
    theta_matrix = (torch.pi / 2) - torch.acos(sim_matrix)
    positive_mask, negative_mask = return_mask(sim_matrix, verb_mask)

    theta_with_margin = theta_matrix.clone()
    theta_with_margin[positive_mask.bool()] -= margin_in_radians  # Subtract margin directly for positives

    logits = theta_with_margin / tau  # Scale with temperature

    # Compute exponential logits for softmax
    exp_logits = torch.exp(logits)
    # pos_exp_logits = (exp_logits * positive_mask).sum(dim=-1)  # Positive term
    pos_exp_logits = exp_logits * positive_mask
    pos_exp_logits = pos_exp_logits.sum(dim=-1)

    # neg_exp_logits = (exp_logits * negative_mask).sum(dim=-1)  # Negative term
    # total_exp_logits = pos_exp_logits + neg_exp_logits
    total_exp_logits = exp_logits.sum(dim=-1)

    # pos_exp_logits = pos_exp_logits + epsilon
    # total_exp_logits = total_exp_logits + epsilon

    # Compute angular loss
    loss = -torch.log(pos_exp_logits / total_exp_logits)
    angular_loss = loss.mean()

    return angular_loss



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 = []
    mean_acc = []
    with torch.no_grad():
        for data in metric_logger.log_every(data_loader, 100, header):
            total_its += 1
            image, target, source_type, sentences, sentences1, attentions = data
            image, sentences, sentences1, attentions = image.cuda(non_blocking=True), \
                                                        sentences.cuda(non_blocking=True), \
                                                        sentences1.cuda(non_blocking=True), \
                                                        attentions.cuda(non_blocking=True)
            sentences = sentences.squeeze(1)
            sentences1 = sentences1.squeeze(1)
            attentions = attentions.squeeze(1)
            target = target.data.numpy()

            for j in range(sentences.size(-1)):

                last_hidden_states = bert_model(sentences[:, :, j], attention_mask=attentions[:, :, j])[0]
                embedding = last_hidden_states.permute(0, 2, 1)  # (B, 768, N_l) to make Conv1d happy
                embedding1 = embedding
                loss_contra, loss_lansim, output = model(image, embedding, embedding1, l_mask=attentions[:, :, j].unsqueeze(-1), training_flag=True)

                output_mask = output.argmax(1).cpu().data.numpy()

                if source_type[0] == 'zero':
                    incorrect_num = np.sum(output_mask)
                    if incorrect_num == 0:
                        acc = 1
                    else:
                        acc = 0
                    mean_acc.append(acc)
                else:
                    I, U = computeIoU(output_mask, target)
                    if U == 0:
                        this_iou = 0.0
                    else:
                        this_iou = I*1.0/U
                    mean_IoU.append(this_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] += (this_iou >= eval_seg_iou)

                    seg_total += 1


    mIoU = np.mean(mean_IoU)
    mean_acc = np.mean(mean_acc)
    precs = []
    print('Final results:')
    results_str = ''
    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)
    results_str += '    mean IoU = %.2f\n' % (mIoU * 100.)
    print(results_str)
    if args.dataset == 'ref-zom':
        print('Mean accuracy for one-to-zero sample is %.2f\n' % (mean_acc*100))

    return mIoU, 100 * cum_I / cum_U, precs


def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq,
                    iterations, bert_model, metric_learning, args):
    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
    mlw = args.metric_loss_weight
    metric_mode = args.metric_mode


    for data in metric_logger.log_every(data_loader, print_freq, header):
        total_its += 1
        image, target, source_type, sentences, sentences_masked, attentions, pos_sent, pos_attn_mask, pos_type = data
        source_type = np.array(source_type)
        pos_type = np.array(pos_type)
        target_flag = torch.tensor(np.where(source_type == 'zero', 0, 1))
        if args.addzero :
            hardpos_flag = torch.tensor(np.where(pos_type == 'hardpos', 1, 0))
        else : 
            # default option for training : only include one, many targets!
            hardpos_flag = torch.tensor(np.where((source_type != 'zero') & (pos_type == 'hardpos'), 1, 0))

        sentences = sentences.squeeze(1)
        sentences_masked = sentences_masked.squeeze(1)
        attentions = attentions.squeeze(1)
        pos_sent = pos_sent.squeeze(1)
        pos_attn_mask = pos_attn_mask.squeeze(1)

        ## ver 1 : hardpos flag outside the model
        verb_masks = [] 
        cl_masks = []
        images = []  
        targets = []
        sentences_ = []
        sentences_masked_ = []
        attentions_ = []
                
        # print(image.shape, sentences.shape, pos_attn_mask.shape)
        for idx in range(len(image)) : 
            # Append original data
            sentences_.append(sentences[idx])
            sentences_masked_.append(sentences_masked[idx])
            images.append(image[idx])
            targets.append(target[idx])
            attentions_.append(attentions[idx])

            if hardpos_flag[idx] :
                verb_masks.extend([1, 1])
                cl_masks.extend([1, 0])
                sentences_.append(pos_sent[idx])
                sentences_masked_.append(sentences_masked[idx])
                images.append(image[idx])
                targets.append(target[idx])
                attentions_.append(pos_attn_mask[idx])
            else:
                verb_masks.append(0)
                cl_masks.append(1)                    

        image, target, sentences, sentences_masked, attentions, verb_masks, cl_masks = \
                                                        torch.stack(images).cuda(non_blocking=True),\
                                                        torch.stack(targets).cuda(non_blocking=True),\
                                                        torch.stack(sentences_).cuda(non_blocking=True),\
                                                        torch.stack(sentences_masked_).cuda(non_blocking=True),\
                                                        torch.stack(attentions_).cuda(non_blocking=True),\
                                                        torch.tensor(verb_masks, dtype=torch.bool, device='cuda'),\
                                                        torch.tensor(cl_masks, dtype=torch.bool, device='cuda')

        ## apply bert language enc
        last_hidden_states = bert_model(sentences, attention_mask=attentions)[0]  # (B+P, 10, 768)
        last_hidden_states1 = bert_model(sentences_masked, attention_mask=attentions)[0]  # (B+P, 10, 768)
        embedding = last_hidden_states.permute(0, 2, 1)  # (B+P, 768, N_l) to make Conv1d happy
        embedding1 = last_hidden_states1.permute(0, 2, 1)  # (B+P, 768, N_l) to make Conv1d happy
        attentions = attentions.unsqueeze(dim=-1)  # (batch, N_l, 1)
        
        ########################### TODO ##################################
        
        loss_contra, loss_lansim, output, metric_tensors = model(image, embedding, embedding1, l_mask=attentions, cl_masks=cl_masks, target_flag=target_flag, training_flag=True)

        loss_seg = criterion(output[cl_masks], target[cl_masks]) 
        
        if metric_learning and sum(hardpos_flag) > 0 :
            metric_loss = UniAngularLogitContrastLoss(metric_tensors, verb_masks, m=args.margin_value, tau=args.temperature, verbonly=True, args=args)
            total_weight = 1 + 0.01 + 0.01 + mlw
            loss = (loss_seg + loss_lansim * 0.01 + loss_contra * 0.01 + metric_loss * mlw) / total_weight            
        else :     
            loss = loss_seg + loss_lansim * 0.01 + loss_contra * 0.01
            
        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"])
        metric_logger.update(loss_seg=loss_seg.item(), lr=optimizer.param_groups[0]["lr"])
        metric_logger.update(loss_lansim=loss_lansim.item(), lr=optimizer.param_groups[0]["lr"])
        metric_logger.update(loss_contra=loss_contra.item(), lr=optimizer.param_groups[0]["lr"])

        del image, target, sentences, sentences_masked, attentions, verb_masks, cl_masks, loss, output, metric_tensors, data

        if bert_model is not None:
            del last_hidden_states, embedding, last_hidden_states1, embedding1
        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("_".join([args.dataset, args.splitBy]), args.model_id))

    dataset, num_classes = get_dataset("train",
                                       transform(args=args),
                                       args=args,
                                       eval_mode=False)
    dataset_test, _ = get_dataset(args.split,
                                  get_transform(args=args),
                                  args=args, eval_mode=True)

    # 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()
    if args.dataset == 'ref-zom':
        train_sampler = Refzom_DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank,
                                                                        shuffle=True)
    else:
        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_backbone, 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

    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


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

    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]},
        {"params": [p for p in single_model.contrastive.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)])},
    ]
    # {"params": reduce(operator.concat,
    #                     [[p for p in single_bert_model.encoder.layer[i].parameters()
    #                     if p.requires_grad] for i in range(10)]), 'lr': args.lr/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

    # iou, overallIoU = evaluate(model, data_loader_test, bert_model)
    # training loops
    for epoch in range(max(0, resume_epoch+1), args.epochs):
        data_loader.sampler.set_epoch(epoch)
        itrs_temp, loss_log = train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq,
                        iterations, bert_model, metric_learning=args.metric_learning, args=args)
        mean_IoU, overall_IoU, precs = evaluate(model, data_loader_test, bert_model)

        print('Average object IoU {}'.format(mean_IoU))
        print('Overall IoU {}'.format(overall_IoU))


        save_checkpoint = (best_oIoU < overall_IoU)
        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 = overall_IoU
            print('The best_performance is {}'.format(best_oIoU))


        if utils.is_main_process():
            writer.add_scalar('val/mIoU', mean_IoU, epoch)
            writer.add_scalar('val/oIoU', overall_IoU, 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.flush()
    

    # summarize
    print('The final_best_performance is {}'.format(best_oIoU))
    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()
    # set up distributed learning
    
    if "LOCAL_RANK" in os.environ:
        local_rank = int(os.environ["LOCAL_RANK"])
    else:
        local_rank = 0  # Default value for non-distributed mode

    print(f"Local Rank: {local_rank}, World Size: {os.environ.get('WORLD_SIZE', '1')}")


    utils.init_distributed_mode(args)
    print('Image size: {}'.format(str(args.img_size)))
    print('Metric Learning Ops')
    print('metric learning flag : ', args.metric_learning)
    print('metric loss weight : ', args.metric_loss_weight)
    print('metric mode and hardpos selection : ', args.metric_mode, args.hp_selection)
    print('margin value : ', args.margin_value)
    print('temperature : ', args.temperature)
    main(args)