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 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 def get_dataset(image_set, transform, args): if args.dataset == "grefcoco": # from data.dataset_grefer import GReferDataset from data.dataset_grefer_mosaic 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') ) fpath = os.path.join('coco-data-vis-mosaic', args.model_id, 'train') MosaicVisualization(ds, fpath) else : from data.dataset_refer_bert_mosaic import ReferDataset ds = ReferDataset(args, split=image_set ) fpath = os.path.join('coco-data-vis-mosaic', args.model_id, image_set) 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("_".join([args.dataset, args.splitBy]), 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) 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 aug_prob = args.aug.aug_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)