ASDA/engine/engine_gref_sbert_oiou.py

import time
import matplotlib as mpl
mpl.use('Agg')

import numpy as np
import torch
import torch.nn.parallel
import torch.optim
from torch.autograd import Variable
from torch.cuda.amp import autocast as autocast

from model.model_sbert_gref import *
from dataset.data_loader import *
from utils.losses import *
from utils.parsing_metrics import *
from utils.utils import *
from utils.utils import dice_loss, sigmoid_focal_loss

use_cuda = torch.cuda.is_available()
print("use_cuda, ", use_cuda)


def return_mask(emb_distance, verb_mask=None, rows_to_filter=None, cols_to_filter=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
    negative_mask = negative_mask.clone() 
    
    if rows_to_filter is not None and cols_to_filter is not None :
        for row, col in zip(rows_to_filter, cols_to_filter):
            negative_mask[row * 2, col * 2] = 0
            negative_mask[row * 2, col * 2 + 1] = 0
            negative_mask[row * 2 + 1, col * 2] = 0
            negative_mask[row * 2 + 1, col * 2 + 1] = 0

    return positive_mask, negative_mask


def UniAngularLogitContrastLoss(total_fq, verb_mask, rows_to_filter, cols_to_filter, alpha=0.5, verbonly=True, m=0.5, tau=0.05, args=None):        
    _, 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)
    # print("sim_matrix : ", sim_matrix)
    # print("theta_matrix : ", theta_matrix)
        
    positive_mask, negative_mask = return_mask(sim_matrix, verb_mask, rows_to_filter, cols_to_filter)
    # print("positive_mask : ", positive_mask)
    # print("negative_mask : ", negative_mask)
    # print("positive_mask requires_grad:", positive_mask.requires_grad, 
    #     "device:", positive_mask.device, "dtype:", positive_mask.dtype)
    # print("negative_mask requires_grad:", negative_mask.requires_grad, 
    #     "device:", negative_mask.device, "dtype:", negative_mask.dtype)


    theta_with_margin = theta_matrix.clone()
    theta_with_margin[positive_mask.bool()] -= margin_in_radians  
    logits = theta_with_margin / tau  # Scale with temperature
    
    # Compute exp logits for softmax
    exp_logits = torch.exp(logits) 
    pos_exp_logits = exp_logits * positive_mask
    pos_exp_logits = pos_exp_logits.sum(dim=-1)
    neg_exp_logits = exp_logits * negative_mask
    neg_exp_logits = neg_exp_logits.sum(dim=-1)
    
    total_exp_logits = pos_exp_logits + neg_exp_logits

    positive_loss = -torch.log(pos_exp_logits/ total_exp_logits)
    angular_loss = positive_loss.mean()
    # print("angular_loss : ", angular_loss)

    return angular_loss, B_


def train_epoch(rank, args, train_loader, model, optimizer, epoch, scaler, logger):
    print('train at epoch %d'%epoch)
    batch_time = AverageMeter()
    losses = AverageMeter()
    dice_losses = AverageMeter()
    sigmoid_focal_losses = AverageMeter()
    cos_losses = AverageMeter()
    model.train()
    end = time.time()
    
    # argument for verb-centric radial contrastive loss
    mlw = args.metric_loss_weight
    metric_mode = args.metric_mode
    filter_thres = args.filter_thres
    metric_learning = args.metric_learning
    
    for batch_idx, (imgs, word_id, word_mask, bbox, seg_map, params) in enumerate(train_loader):
        B = imgs.size(0) # Original Batch size
        
        hp_word_id = params['hp_word_id']
        hp_word_mask = params['hp_word_mask']
        hp_bert_embs = params['hardpos_emb'].cuda(non_blocking=True).squeeze(1)
        pos_type = np.array(params['pos_type'])

        pos_mask = torch.tensor(np.where(pos_type == 'hardpos', 1, 0))
        
        # print(hp_bert_embs.shape)
        # print(imgs.shape, word_id.shape, word_mask.shape, seg_map.shape)

        # hardpos flag outside the model
        verb_masks = [] 
        cl_masks = []
        images = []  
        targets = []
        sentences_ = []
        sentences_masked_ = []
                
        for idx in range(len(imgs)) :
            sentences_.append(word_id[idx])
            sentences_masked_.append(word_mask[idx])
            images.append(imgs[idx])
            targets.append(seg_map[idx])

            # If verb exists, process it
            if pos_mask[idx] :
                verb_masks.extend([1, 1])  # Both original sentence and verb are marked
                cl_masks.extend([1, 0])    # Only original sentence get marked
                sentences_.append(hp_word_id[idx])
                sentences_masked_.append(hp_word_mask[idx])
                images.append(imgs[idx])
                targets.append(seg_map[idx])
            else:
                verb_masks.append(0)
                cl_masks.append(1)

        imgs, seg_map, word_id, word_mask, verb_masks, cl_masks = \
                                                        torch.stack(images).cuda(rank, non_blocking=True),\
                                                        torch.stack(targets).cuda(rank, non_blocking=True),\
                                                        torch.stack(sentences_).cuda(rank, non_blocking=True),\
                                                        torch.stack(sentences_masked_).cuda(rank, non_blocking=True),\
                                                        torch.tensor(verb_masks, dtype=torch.bool).cuda(rank, non_blocking=True),\
                                                        torch.tensor(cl_masks, dtype=torch.bool).cuda(rank, non_blocking=True)

        image = Variable(imgs)
        word_id = Variable(word_id)
        word_mask = Variable(word_mask)
        seg_map = Variable(seg_map)
        verb_masks = Variable(verb_masks)
        cl_masks = Variable(cl_masks)        
        
        if hp_bert_embs.numel() > 0 :
            mask = ~torch.all(hp_bert_embs == 0, dim=1) 
            hp_bert_embs = hp_bert_embs[mask]
            # print(hp_bert_embs.shape, hp_bert_embs.requires_grad, hp_bert_embs.device)
            norms = torch.norm(hp_bert_embs, dim=-1, keepdim=True)
            normed_embs = hp_bert_embs / norms
            cosime_sim = torch.mm(normed_embs, normed_embs.T)
            rows_to_filter, cols_to_filter = torch.where(cosime_sim > filter_thres) 

            # print(normed_embs, normed_embs.requires_grad, normed_embs.device)
            # print(cosime_sim, cosime_sim.requires_grad, cosime_sim.device)
            # print("rows_to_filter : ", rows_to_filter, rows_to_filter.requires_grad)
            # print("cols_to_filter : ", cols_to_filter, cols_to_filter.requires_grad)
            
        with autocast():
            mask_out_all, metric_tensors = model(image, word_id, word_mask)
            loss = 0.            
            
            # get mask and seg_map for calculating existing loss function (iou loss, dice loss, sigmoid focal loss)
            mask_out = mask_out_all[cl_masks]
            seg_map_cl = seg_map[cl_masks]
            
            mask_out_np = mask_out.data.cpu().numpy() # [bs, 1, 208, 208]
            seg_map_np = seg_map_cl.cpu().numpy() # [bs, 1, 208, 208]
            seg_iou = cal_seg_iou_loss(seg_map_np, mask_out_np, args.seg_thresh)
         
            dice_loss_ = dice_loss(mask_out, seg_map_cl)
            sigmoid_focal_loss_ = sigmoid_focal_loss(mask_out, seg_map_cl)

            dice_weight, focal_weight = 1.0, 1.0
            loss = (dice_weight * dice_loss_) + (focal_weight * sigmoid_focal_loss_)
            
            # get angular contrastive loss, which involves original & verb pharase pairs (only for pairs where hardpos verb phrase exists)
            if metric_learning and sum(pos_mask) > 1 :
                metric_weight = mlw
                # NS means number of orig-verb pair where verb phrase exists.
                metric_loss, NS = UniAngularLogitContrastLoss(metric_tensors, verb_masks, rows_to_filter, cols_to_filter, m=args.margin_value, tau=args.temperature, verbonly=True, args=args)
                loss += metric_weight * metric_loss

        optimizer.zero_grad()
        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()

        losses.update(loss.item(), B)
        dice_losses.update(dice_loss_.item(), B)
        sigmoid_focal_losses.update(sigmoid_focal_loss_.item(), B)
        cos_losses.update(seg_iou.mean().item(), B)
        
        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if rank == 0 and batch_idx % args.print_freq == 0:
            print_str = 'Epoch: [{0}][{1}/{2}]\t' \
                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
                'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \
                'dice_losses {dice_losses.val:.4f} ({dice_losses.avg:.4f})\t' \
                'sigmoid_focal_losses {sigmoid_focal_losses.val:.4f} ({sigmoid_focal_losses.avg:.4f})\t' \
                'IoU {cos_loss.val:.4f} ({cos_loss.avg:.4f})\t' \
                .format(epoch, batch_idx, len(train_loader), batch_time=batch_time, loss=losses, dice_losses=dice_losses, sigmoid_focal_losses=sigmoid_focal_losses, cos_loss=cos_losses)
            print(print_str)
            logger.info(print_str)

    return losses.avg

def validate_epoch(args, val_loader, model, logger, mode='val'):
    print('begin test')
    batch_time = AverageMeter()
    miou = AverageMeter()
    miou_seg = AverageMeter()

    prec=dict()
    thresholds = np.arange(0.5, 1, 0.05)

    for thresh in thresholds:
        prec[thresh]= AverageMeter()

    model.eval()
    end = time.time()
    idx = 0

    t_all = []
    total_intersection = 0.0
    total_union = 0.0
    
    for batch_idx, (imgs, word_id, word_mask, bbox, seg_map, ratio, dw, dh, im_id, phrase, draw_img) in enumerate(val_loader):
        
        imgs = imgs.cuda(0)
        word_id = word_id.cuda(0)
        word_mask = word_mask.cuda(0)
        seg_map = seg_map.cuda(0)
        image = Variable(imgs)
        word_id = Variable(word_id)
        word_mask = Variable(word_mask)
        seg_map = Variable(seg_map)

        t1 = time.time()
        with torch.no_grad():
            mask_out, _ = model(image, word_id, word_mask)
            mask_out = mask_out.sigmoid()

        t2 = time.time()
        t_all.append(t2-t1)

        ## test: convert pred, gt box to original scale with meta-info
        ih = seg_map.shape[-2]
        iw = seg_map.shape[-1]
        nh = int(ih * ratio)
        nw = int(iw * ratio)
        top, bottom = int(dh[0]), nh + int(dh[0])
        left, right = int(dw[0]), nw + int(dw[0])
        ratio = float(ratio)
        new_shape = (iw, ih)
        
        ## revert image for visualization
        seg_map_np = seg_map[0,:,:,:].data.cpu().numpy().transpose(1,2,0)
        seg_map_np = cv2.resize(seg_map_np, new_shape, interpolation=cv2.INTER_CUBIC)
        img_np = imgs[0,:,top:bottom,left:right].data.cpu().numpy().transpose(1,2,0)
        img_np = cv2.resize(img_np, new_shape, interpolation=cv2.INTER_CUBIC)

        img_np = Variable(torch.from_numpy(img_np.transpose(2,0,1)).cuda().unsqueeze(0))
        
        # seg
        mask_out = mask_out[0].data.cpu().numpy().transpose(1,2,0)
        mask_out = cv2.resize(mask_out, (args.size, args.size))
        mask_out_np = mask_out[top:bottom, left:right]
        mask_out_np = cv2.resize(mask_out_np, new_shape)
        # seg_iou, seg_prec = cal_seg_iou(seg_map[0].cpu().numpy(), mask_out_np, args.seg_thresh)
        seg_iou, seg_prec, inter_sum, union_sum = cal_seg_iou2(seg_map_np, mask_out_np, args.seg_thresh)

        miou_seg.update(seg_iou, imgs.size(0))
        total_intersection += inter_sum
        total_union += union_sum

        for thresh in thresholds:
            prec[thresh].update(seg_prec[thresh], imgs.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()
        if batch_idx % 1000 == 0:
            print_str = '[{0}/{1}]\t' \
                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \
                'seg_iu {seg.val:.4f} ({seg.avg:.4f})\t' \
                .format( \
                    batch_idx, len(val_loader), batch_time=batch_time, seg=miou_seg)
            print(print_str)
            logger.info(print_str)  
        idx = idx + 1
    overall_iou = (total_intersection + 1e-10) / (total_union + 1e-10)

    print("Mean IoU:", miou_seg.avg)
    print("Overall IoU:", overall_iou)
    logger.info("Mean IoU: %.4f" % miou_seg.avg)
    logger.info("Overall IoU: %.4f" % overall_iou)
 
    for thresh in thresholds:
            print("prec@%f: %f"%(thresh,float(prec[thresh].avg)))
            logger.info("prec@%f:%f"%(thresh,float(prec[thresh].avg)))
    # logger.info("%f,%f"%(float(miou.avg), miou_seg.avg))
    return miou_seg.avg, overall_iou, prec