engine/engine.py

import os
import time
import math
from tqdm import tqdm
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.cuda.amp as amp
import torch.distributed as dist
import torch.nn.functional as F
import wandb
from loguru import logger
from utils.dataset import tokenize
from utils.misc import (AverageMeter, ProgressMeter, concat_all_gather,
                        trainMetricGPU)


def return_mask(emb_distance):
    B_, B_ = emb_distance.shape
    positive_mask = torch.zeros_like(emb_distance)
    for i in range(B_//2):
        positive_mask[2*i, 2*i+1] = 1
        positive_mask[2*i+1, 2*i] = 1
    positive_mask.fill_diagonal_(1)
    negative_mask = torch.ones_like(emb_distance) - positive_mask

    return positive_mask, negative_mask


def MetricLoss(embeddings, n_pos, alpha = 0.5, args = None):
    # embeddings: ((2*B), C, (H*W))
    # n_pos : chunk size of positive pairs
    # args: args
    # returns: loss
    metric_loss = 0

    # flatten embeddings
    B_, C, HW = embeddings.shape
    emb = torch.mean(embeddings, dim=-1) # (2*B, C)
    emb_i = emb.unsqueeze(1).repeat(1, B_, 1) # (2*B, 2*B, C)
    emb_j = emb.unsqueeze(0).repeat(B_, 1, 1) # (2*B, 2*B, C)
    emb_distance = torch.norm(emb_i - emb_j, dim=-1) # (2*B, 2*B)
    assert torch.sum(torch.diag(emb_distance[:B_, :B_])) == 0, \
    "Diagonals are not zero. please check the permutation on the batch"
    # print("distance metrix : ", emb_distance)

    positive_mask, negative_mask = return_mask(emb_distance)
    positive_loss = torch.sum(emb_distance * positive_mask) / B_**2 #B_

    # negative pairs and loss
    # negative_mask = torch.ones_like(emb_distance) - positive_mask
    negative_loss = -1.0 * torch.log(torch.sum(emb_distance * negative_mask) / B_**2) #(B_**2 - 2*B_))

    # print(positive_mask, negative_mask)

    metric_loss = alpha * positive_loss + (1-alpha) * negative_loss

    return metric_loss


def AngularMetricLoss(embeddings, n_pos, alpha = 0.5, args = None, mask = None):
    # embeddings: ((2*B), C, (H*W))
    # n_pos : chunk size of positive pairs
    # args: args
    # returns: loss
    geometric_loss = 0

    # flatten embeddings
    B_, C, HW = embeddings.shape
    emb = torch.mean(embeddings, dim=-1) # (2*B, C)
    emb_i = emb.unsqueeze(1).repeat(1, B_, 1) # (2*B, 2*B, C)
    emb_j = emb.unsqueeze(0).repeat(B_, 1, 1) # (2*B, 2*B, C)
    sim = nn.CosineSimilarity(dim=-1, eps=1e-6)
    sim_matrix = sim(emb_i, emb_j).reshape(B_, B_) # (2*B , 2*B)
    sim_matrix = torch.clamp(sim_matrix, min=-0.9999, max=0.9999)
    #print("similarity metrix : ", sim_matrix)
    phi = torch.acos(sim_matrix) # (2*B, 2*B)
    #print("phi metrix : ", phi)
    #print(args.batch_size, B_)
    assert (B_ == args.batch_size * 2 * args.ngpus_per_node), \
    "B_ must be 2x batch_size. please check the inputs."

    # positive pairs and loss
    positive_mask, negative_mask = return_mask(sim_matrix)
    # positive_mask = torch.zeros_like(sim_matrix)
    # for i in range(B_//2):
    #     positive_mask[2*i, 2*i+1] = 1
    #     positive_mask[2*i+1, 2*i] = 1
    # positive_mask.fill_diagonal_(1)
    positive_loss = torch.sum((phi**2) * positive_mask) / B_**2

    # negative pairs and loss
    # negative_mask = torch.ones_like(sim_matrix) - positive_mask
    phi_mask = phi < args.phi_threshold
    negative_loss = (args.phi_threshold - phi)**2 
    #print(negative_mask * phi_mask)
    negative_loss = torch.sum(negative_loss * negative_mask * phi_mask) / B_**2

    #print("pos loss, neg loss : ", positive_loss, negative_loss)

    geometric_loss = alpha * positive_loss + (1-alpha) * negative_loss

    return geometric_loss


def train(train_loader, model, optimizer, scheduler, scaler, epoch, args):
    batch_time = AverageMeter('Batch', ':2.2f')
    data_time = AverageMeter('Data', ':2.2f')
    lr = AverageMeter('Lr', ':1.6f')
    loss_meter = AverageMeter('Loss', ':2.4f')
    iou_meter = AverageMeter('IoU', ':2.2f')
    pr_meter = AverageMeter('Prec@50', ':2.2f')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, lr, loss_meter, iou_meter, pr_meter],
        prefix="Training: Epoch=[{}/{}] ".format(epoch, args.epochs))
    metric_learning = args.metric_learning
    # mix_distance_angular = args.mix_distance_angular
    # positive_strength = args.positive_strength
    # angular_loss_weight = args.metric_loss_weight * math.exp(-3.0 * (1-epoch/args.epochs)**2)
    #print("epoch : ", epoch, ", angular loss weight : ", angular_loss_weight)
    # distance_loss_weight = args.distance_loss_weight

    model.train()
    time.sleep(2)
    end = time.time()

    # size_list = [320, 352, 384, 416, 448, 480, 512]
    # idx = np.random.choice(len(size_list))
    # new_size = size_list[idx]

    for i, (image, text, target) in enumerate(train_loader):
        data_time.update(time.time() - end)
        # data
        image = image.cuda(non_blocking=True)
        text = text.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True).unsqueeze(1)

        # # multi-scale training
        # image = F.interpolate(image, size=(new_size, new_size), mode='bilinear')

        # masking when params exists
        #mask_tensor = torch.tensor([True if params[i] else False for i in range(len(params))], dtype=torch.bool)

        # forward
        with amp.autocast():
            pred, target, loss = model(image, text, target)
            # pred, target, CE_loss, metric_tensor = model(image, text, target)
                
        # gather tensors
        # metric_tensor = concat_all_gather(metric_tensor)
        
        # get metric loss
        #print("gathered tensor shape : ", metric_tensor.shape)
        # metric_loss = 0
        # if metric_learning:
        #     metric_loss += \
        #         angular_loss_weight * AngularMetricLoss(metric_tensor, 2, alpha=positive_strength, args = args) #, mask=mask_tensor) 
        #     if mix_distance_angular:
        #         metric_loss += \
        #             distance_loss_weight * MetricLoss(metric_tensor, 2, alpha=positive_strength, args = args) #, mask=mask_tensor)
        #     loss = (CE_loss + metric_loss) / \
        #             (1 + angular_loss_weight*metric_learning + \
        #             distance_loss_weight*metric_learning*mix_distance_angular)
        # else :
            # loss = CE_loss

        # backward
        optimizer.zero_grad()
        scaler.scale(loss).backward()
        #loss.backward()
        if args.max_norm:
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
        #optimizer.step()
        scaler.step(optimizer)
        scaler.update()
        #dist.barrier()

        # metric
        iou, pr5 = trainMetricGPU(pred, target, 0.35, 0.5)
        dist.all_reduce(loss.detach())
        dist.all_reduce(iou)
        dist.all_reduce(pr5)
        loss = loss / dist.get_world_size()
        iou = iou / dist.get_world_size()
        pr5 = pr5 / dist.get_world_size()

        loss_meter.update(loss.item(), image.size(0))
        iou_meter.update(iou.item(), image.size(0))
        pr_meter.update(pr5.item(), image.size(0))
        lr.update(scheduler.get_last_lr()[-1])
        batch_time.update(time.time() - end)
        end = time.time()

        if (i + 1) % args.print_freq == 0:
            progress.display(i + 1)
            if dist.get_rank() in [-1, 0]:
                wandb.log(
                    {
                        "time/batch": batch_time.val,
                        "time/data": data_time.val,
                        "training/lr": lr.val,
                        "training/loss": loss_meter.val,
                        "training/iou": iou_meter.val,
                        "training/prec@50": pr_meter.val,
                    },
                    step=epoch * len(train_loader) + (i + 1))
    torch.cuda.empty_cache()


@torch.no_grad()
def validate(val_loader, model, epoch, args):
    iou_list = []
    I_list = []
    U_list = []
    model.eval()
    time.sleep(16)
    for imgs, texts, masks, param in val_loader:
        # data
        imgs = imgs.cuda(non_blocking=True)
        texts = texts.cuda(non_blocking=True)
        # inference
        preds = model(imgs, texts)
        preds = torch.sigmoid(preds)
        if preds.shape[-2:] != imgs.shape[-2:]:
            preds = F.interpolate(preds,
                                  size=imgs.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
        # process one batch
        # for pred, mask_dir, mat, ori_size in zip(preds, param['mask_dir'],
        #                                          param['inverse'],
        #                                          param['ori_size']):
        #     h, w = np.array(ori_size)
        #     mat = np.array(mat)
        #     pred = pred.cpu().numpy()
        #     pred = cv2.warpAffine(pred, mat, (w, h),
        #                           flags=cv2.INTER_CUBIC,
        #                           borderValue=0.)
        #     pred = np.array(pred > 0.35)
        #     mask = cv2.imread(mask_dir, flags=cv2.IMREAD_GRAYSCALE)
        #     mask = mask / 255.
        #     # iou
        #     inter = np.logical_and(pred, mask)
        #     union = np.logical_or(pred, mask)
        #     iou = np.sum(inter) / (np.sum(union) + 1e-6)
        #     iou_list.append(iou)
        #     I_list.append(inter)
        #     U_list.append(union)
        for pred, mask in zip(preds, masks):
            # h, w = np.array(ori_size)
            # mat = np.array(mat)
            pred = pred.cpu().numpy()
            # pred = cv2.warpAffine(pred, mat, (w, h),
            #                       flags=cv2.INTER_CUBIC,
            #                       borderValue=0.)
            pred = np.array(pred > 0.35)
            # mask = cv2.imread(mask_dir, flags=cv2.IMREAD_GRAYSCALE)
            # mask = mask / 255.
            mask = mask.numpy()
            # iou
            inter = np.logical_and(pred, mask)
            union = np.logical_or(pred, mask)
            iou = np.sum(inter) / (np.sum(union) + 1e-6)
            I_list.append(inter)
            U_list.append(union)
            iou_list.append(iou)

    iou_list = np.stack(iou_list)
    iou_list = torch.from_numpy(iou_list).to(imgs.device)
    iou_list = concat_all_gather(iou_list)
    
    I_list = np.stack(I_list)
    I_list = torch.from_numpy(I_list).to(imgs.device)
    I_list = concat_all_gather(I_list)
 
    U_list = np.stack(U_list)
    U_list = torch.from_numpy(U_list).to(imgs.device)
    U_list = concat_all_gather(U_list)

    overall_I = I_list.sum().item()
    overall_U = U_list.sum().item()
    overall_IoU = overall_I / (overall_U + 1e-6)  # to avoid division by zero

    
    prec_list = []
    for thres in torch.arange(0.5, 1.0, 0.1):
        tmp = (iou_list > thres).float().mean()
        prec_list.append(tmp)
    iou = iou_list.mean()
    prec = {}
    temp = '  '
    for i, thres in enumerate(range(5, 10)):
        key = 'Pr@{}'.format(thres * 10)
        value = prec_list[i].item()
        prec[key] = value
        temp += "{}: {:.2f}  ".format(key, 100. * value)
    head = 'Evaluation: Epoch=[{}/{}]  IoU={:.2f}  OIoU={:.4f}'.format(
        epoch, args.epochs, 100. * iou.item(), 100. * overall_IoU)
    logger.info(head + temp)
    
    # return three results : mIoU, oIoU and prec results
    torch.cuda.empty_cache()
    return iou.item(), overall_IoU, prec


@torch.no_grad()
def inference(test_loader, model, args):
    iou_list = []
    I_list = []
    U_list = []

    tbar = tqdm(test_loader, desc='Inference:', ncols=100)
    model.eval()
    time.sleep(2)
    for img, mask, param in tbar:
        # data
        # img = img.cuda(non_blocking=True)
        # mask = cv2.imread(param['mask_dir'][0], flags=cv2.IMREAD_GRAYSCALE)
        img = img.cuda(non_blocking=True)
        mask = mask[0].cpu().numpy()
        
        # dump image & mask
        if args.visualize:
            seg_id = param['seg_id'][0].cpu().numpy()
            img_name = '{}-img.jpg'.format(seg_id)
            mask_name = '{}-mask.png'.format(seg_id)
            cv2.imwrite(filename=os.path.join(args.vis_dir, img_name),
                        img=param['ori_img'][0].cpu().numpy())
            cv2.imwrite(filename=os.path.join(args.vis_dir, mask_name),
                        img=mask)
        # multiple sentences
        for sent in param['sents']:
            # mask = mask / 255.
            text = tokenize(sent, args.word_len, True)
            text = text.cuda(non_blocking=True)
            # inference
            pred = model(img, text)
            pred = torch.sigmoid(pred)
            if pred.shape[-2:] != img.shape[-2:]:
                pred = F.interpolate(pred,
                                     size=img.shape[-2:],
                                     mode='bicubic',
                                     align_corners=True).squeeze()
            # process one sentence
            # h, w = param['ori_size'].numpy()[0]
            # mat = param['inverse'].numpy()[0]
            pred = pred.cpu().numpy()
            # pred = cv2.warpAffine(pred, mat, (w, h),
            #                       flags=cv2.INTER_CUBIC,
            #                       borderValue=0.)
            pred = np.array(pred > 0.35)
            # iou
            inter = np.logical_and(pred, mask)
            union = np.logical_or(pred, mask)
            iou = np.sum(inter) / (np.sum(union) + 1e-6)
            iou_list.append(iou)
            I_list.append(inter)
            U_list.append(union)
            # dump prediction
            if args.visualize:
                pred = np.array(pred*255, dtype=np.uint8)
                sent = "_".join(sent[0].split(" "))
                pred_name = '{}-iou={:.2f}-{}.png'.format(seg_id, iou*100, sent)
                cv2.imwrite(filename=os.path.join(args.vis_dir, pred_name),
                            img=pred)
    logger.info('=> Metric Calculation <=')
    iou_list = np.stack(iou_list)
    iou_list = torch.from_numpy(iou_list).to(img.device)

    I_list = np.stack(I_list)
    I_list = torch.from_numpy(I_list).to(img.device)
    U_list = np.stack(U_list)
    U_list = torch.from_numpy(U_list).to(img.device)
    overall_I = I_list.sum().item()
    overall_U = U_list.sum().item()
    overall_IoU = overall_I / (overall_U + 1e-6)  # to avoid division by zero

    prec_list = []
    for thres in torch.arange(0.5, 1.0, 0.1):
        tmp = (iou_list > thres).float().mean()
        prec_list.append(tmp)
    iou = iou_list.mean()
    prec = {}
    for i, thres in enumerate(range(5, 10)):
        key = 'Pr@{}'.format(thres*10)
        value = prec_list[i].item()
        prec[key] = value
    logger.info('IoU={:.2f}  OIoU={:.4f}'.format(100.*iou.item(), 100. * overall_IoU))
    for k, v in prec.items():
        logger.info('{}: {:.2f}.'.format(k, 100.*v))

    return iou.item(), overall_IoU, prec