import datetime import os import time import torch import torch.utils.data from torch import nn from functools import reduce import operator from bert.multimodal_bert import MultiModalBert import torchvision from lib import multimodal_segmentation_ppm import transforms as T import utils import numpy as np import torch.nn.functional as F import gc from collections import OrderedDict import torch.backends.cudnn as cudnn #from ffrecord.torch import DataLoader,Dataset from modeling.MaskFormerModel import MaskFormerHead from addict import Dict from mask2former_utils.criterion import SetCriterion, Criterion from mask2former_utils.matcher import HungarianMatcher from bert.modeling_bert import BertLMPredictionHead, BertEncoder class WrapperModel(nn.Module): def __init__(self, image_model, language_model, classifier, args) : super(WrapperModel, self).__init__() self.image_model = image_model self.language_model = language_model self.classifier = classifier self.lang_proj = nn.Linear(768,256) config = Dict({ "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "gradient_checkpointing": False, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 512, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, #"max_position_embeddings": 16+20, "model_type": "bert", "num_attention_heads": 8, "num_hidden_layers": 8, "pad_token_id": 0, "position_embedding_type": "absolute", "transformers_version": "4.6.0.dev0", "type_vocab_size": 2, "use_cache": True, "vocab_size": 30522 }) self.mlm_transformer = BertEncoder(config) self.lang_proj = nn.Linear(768,256) self.mlm_vis_proj = nn.Conv2d(1024,512,1) self.mlm_lang_proj = nn.Linear(768,512) #print(vis_proj) self.mlm_head = BertLMPredictionHead(config) assert args.img_size % 4 == 0 num_img_tokens = 20 + ((args.img_size // 4)//8) ** 2 print(num_img_tokens) self.mlm_pos_embeds = nn.Embedding(num_img_tokens+1, 512) self.mlm_modal_embeds = nn.Embedding(3, 512) self.mlm_mask_embed = nn.Embedding(1, 512) self.mlm_pos_mlp = nn.Sequential( nn.Linear(2, 512), nn.LayerNorm(512), nn.Linear(512,512), nn.GELU() ) def _get_binary_mask(self, target): # 返回每类的binary mask y, x = target.size() target_onehot = torch.zeros(self.num_classes + 1, y, x) target_onehot = target_onehot.scatter(dim=0, index=target.unsqueeze(0), value=1) return target_onehot[1:] def semantic_inference(self, mask_cls, mask_pred): mask_cls = F.softmax(mask_cls, dim=1)[...,1:] mask_pred = mask_pred.sigmoid() semseg = torch.einsum("bqc,bqhw->bchw", mask_cls, mask_pred) return semseg def forward(self, image, sentences, attentions, mlm_targets, mlm_masks, position): input_shape = image.shape[-2:] l_mask = attentions.unsqueeze(dim=-1) i0, Wh, Ww = self.image_model.forward_stem(image) l0, extended_attention_mask = self.language_model.forward_stem(mlm_targets.squeeze(1), attentions) i1 = self.image_model.forward_stage1(i0, Wh, Ww) l1 = self.language_model.forward_stage1(l0, extended_attention_mask) i1_residual, H, W, i1_temp, Wh, Ww = self.image_model.forward_pwam1(i1, Wh, Ww, l1, l_mask) l1_residual, l1 = self.language_model.forward_pwam1(i1, l1, extended_attention_mask) i1 = i1_temp i2 = self.image_model.forward_stage2(i1, Wh, Ww) l2 = self.language_model.forward_stage2(l1, extended_attention_mask) i2_residual, H, W, i2_temp, Wh, Ww = self.image_model.forward_pwam2(i2, Wh, Ww, l2, l_mask) l2_residual, l2 = self.language_model.forward_pwam2(i2, l2, extended_attention_mask) i2 = i2_temp i3 = self.image_model.forward_stage3(i2, Wh, Ww) l3 = self.language_model.forward_stage3(l2, extended_attention_mask) i3_residual, H, W, i3_temp, Wh, Ww = self.image_model.forward_pwam3(i3, Wh, Ww, l3, l_mask) l3_residual, l3 = self.language_model.forward_pwam3(i3, l3, extended_attention_mask) i3 = i3_temp i4 = self.image_model.forward_stage4(i3, Wh, Ww) l4 = self.language_model.forward_stage4(l3, extended_attention_mask) i4_residual, H, W, i4_temp, Wh, Ww = self.image_model.forward_pwam4(i4, Wh, Ww, l4, l_mask) l4_residual, l4 = self.language_model.forward_pwam4(i4, l4, extended_attention_mask) i4 = i4_temp #i1_residual, i2_residual, i3_residual, i4_residual = features #x = self.classifier(i4_residual, i3_residual, i2_residual, i1_residual) #x = F.interpolate(x, size=input_shape, mode='bilinear', align_corners=True) outputs = {} outputs['s1'] = i1_residual outputs['s2'] = i2_residual outputs['s3'] = i3_residual outputs['s4'] = i4_residual predictions, mask_features = self.classifier(outputs) #print(target_reshape.shape) #tmp = np.argwhere(target_reshape[:, 0].detach().cpu().numpy()).reshape(-1, target_reshape.shape[2]*target_reshape[3], 3) #centroid = tmp.mean(1) #print(centroid) #centroid_x, centroid_y = int(centroid[1]), int(centroid[0]) #last_hidden_states = brt_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 l0, extended_attention_mask = self.language_model.forward_stem(sentences, attentions) l1 = self.language_model.forward_stage1(l0, extended_attention_mask) l2 = self.language_model.forward_stage2(l1, extended_attention_mask) l3 = self.language_model.forward_stage3(l2, extended_attention_mask) l4 = self.language_model.forward_stage4(l3, extended_attention_mask) mlp_embed = self.mlm_pos_mlp(position) #print(centroid_x, centroid_y) mlm_targets = torch.where( mlm_masks > 0, mlm_targets, torch.ones_like(mlm_targets) * (-1) ) #print(x_c4[target_reshape[:, [0]].bool()].shape) vis_features = self.mlm_vis_proj(i4_residual).flatten(2).permute(0,2,1) #print(l4.shape) lang_features = self.mlm_lang_proj(l4) #print(lang_features.shape, vis_features.shape, mlp_embed.shape) mm_features = torch.cat([lang_features, vis_features, mlp_embed.unsqueeze(1)], dim=1) #print(mm_features.shape) #print(mlm_modal_embeds.weight.shape) modal_embeds = torch.cat([self.mlm_modal_embeds.weight[0].unsqueeze(0).repeat(1, lang_features.shape[1], 1), self.mlm_modal_embeds.weight[1].unsqueeze(0).repeat(1, vis_features.shape[1], 1), self.mlm_modal_embeds.weight[2].unsqueeze(0).repeat(1,1,1)], dim=1) #print(modal_embeds.shape) #print(mlm_transformer) #print(attentions.shape) mixed_attention_mask = torch.cat([attentions.unsqueeze(-1), torch.ones(attentions.shape[0], vis_features.shape[1]+1, 1).to(attentions.device)], dim=1) mixed_attention_mask = mixed_attention_mask.permute(0,2,1).unsqueeze(1) mixed_attention_mask = (1-mixed_attention_mask)* -10000.0 head_mask = [None] * 8 #extended_attention_mask = get_extended_attention_mask(mixed_attention_mask, mm_features.shape, mm_features.device) #print(mm_features.shape, mixed_attention_mask.shape, head_mask) #print(mm_features.shape, self.mlm_pos_embeds.weight.shape, self.mlm_modal_embeds.weight.shape) head_features = self.mlm_transformer(mm_features + self.mlm_pos_embeds.weight.unsqueeze(0) + modal_embeds, mixed_attention_mask, head_mask)[0] #print(head_features.shape, attentions.shape) head_features = head_features[:, :20][attentions.bool()] #print(embedding.shape, mask_features.shape) mlm_predictions = self.mlm_head(head_features) mlm_predictions = mlm_predictions.reshape(-1, self.language_model.config.vocab_size) mlm_targets = mlm_targets.squeeze(1)[attentions.bool()] #mlm_loss = mlm_weight * nn.CrossEntropyLoss(ignore_index=-1)(mlm_predictions, mlm_targets) #loss += mlm_loss #mlm_loss_print=mlm_loss.item() return predictions, mask_features, self.lang_proj((l4_residual * l_mask).sum(1)/l_mask.sum(1)), mlm_predictions, mlm_targets # IoU calculation for validation def IoU(pred, gt): #pred = pred.argmax(1) pred = (pred > 0.5) 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_dataset(image_set, transform, args): from data.dataset_refer_bert_mlm import ReferDataset ds = ReferDataset(args, split=image_set, image_transforms=transform, target_transforms=None ) num_classes = 2 return ds, num_classes 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): 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, target, sentences, attentions = image.cuda(non_blocking=True),\ # target.cuda(non_blocking=True),\ # sentences.cuda(non_blocking=True),\ # attentions.cuda(non_blocking=True) image, target, sentences, attentions, mlm_targets, mlm_masks, position = data image, target, sentences, attentions, mlm_targets, mlm_masks, position = image.cuda(non_blocking=True),\ target.cuda(non_blocking=True),\ sentences.cuda(non_blocking=True),\ attentions.cuda(non_blocking=True), \ mlm_targets.cuda(non_blocking=True), \ mlm_masks.cuda(non_blocking=True), \ position.cuda(non_blocking=True) sentences = sentences.squeeze(1) attentions = attentions.squeeze(1) #print("sentences", sentences.shape) #print("attentions", attentions.shape) output, mask_features, avg_lang_feature, mlm_predictions, mlm_targets = model(image, sentences, attentions, mlm_targets, mlm_masks, position) mask_cls_results = output["pred_logits"] mask_pred_results = output["pred_masks"] target_shape = target.shape[-2:] mask_pred_results = F.interpolate(mask_pred_results, size=target_shape, mode='bilinear', align_corners=True) pred_masks = model.module.semantic_inference(mask_cls_results, mask_pred_results) output = pred_masks[0] 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 = '' 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) results_str += ' overall IoU = %.2f\n' % (cum_I * 100. / cum_U) print(results_str) return 100 * iou, 100 * cum_I / cum_U def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, print_freq, iterations, 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 for data in metric_logger.log_every(data_loader, print_freq, header): total_its += 1 #image, target, sentences, attentions = data #image, target, sentences, attentions = image.cuda(non_blocking=True),\ # target.cuda(non_blocking=True),\ # sentences.cuda(non_blocking=True),\ # attentions.cuda(non_blocking=True) image, target, sentences, attentions, mlm_targets, mlm_masks, position = data image, target, sentences, attentions, mlm_targets, mlm_masks, position = image.cuda(non_blocking=True),\ target.cuda(non_blocking=True),\ sentences.cuda(non_blocking=True),\ attentions.cuda(non_blocking=True), \ mlm_targets.cuda(non_blocking=True), \ mlm_masks.cuda(non_blocking=True), \ position.cuda(non_blocking=True) sentences = sentences.squeeze(1) attentions = attentions.squeeze(1) #l_mask = attentions.unsqueeze(dim=-1) output, mask_features, avg_lang_feature, mlm_predictions, mlm_targets = model(image, sentences, attentions, mlm_targets, mlm_masks, position) #print(avg_lang_feature.shape) avg_lang_feature = torch.nn.functional.normalize(avg_lang_feature, dim=1) #print("----") #print(output.shape) #print(mask_features.shape) #print(avg_lang_feature.shape) #print( mlm_predictions.shape) #print(mlm_targets.shape) #print("----") target_shape = target.shape[-2:] output['pred_masks'] = F.interpolate(output['pred_masks'], size=target_shape, mode='bilinear', align_corners=True) if "aux_outputs" in output: for i, aux_outputs in enumerate(output["aux_outputs"]): output['aux_outputs'][i]['pred_masks'] = F.interpolate(output['aux_outputs'][i]['pred_masks'], size=target_shape, mode='bilinear', align_corners=True) # pixel region B, C, H, W = mask_features.shape target_reshape = F.interpolate(target.unsqueeze(1).float(), size=mask_features.shape[-2:], mode='nearest').long() target_reshape = target_reshape.repeat(1, mask_features.shape[1], 1, 1) #print(avg_pos_feature.shape, avg_lang_feature.shape, avg_neg_feature.shape) #cl_loss = 0.0 plic_lang_loss = 0.0 plic_pos_loss = 0.0 plic_neg_loss = 0.0 for i in range(B): if ((target_reshape[[i]] == 0).sum() != 0 and (target_reshape[[i]] == 1).sum() != 0): avg_pos_feature = (mask_features[[i]] * target_reshape[[i]]).sum(-1).sum(-1) / target_reshape[[i]].sum(-1).sum(-1) avg_neg_feature = (mask_features[[i]] * (1.0-target_reshape[[i]])).sum(-1).sum(-1) / (1.0-target_reshape[[i]]).sum(-1).sum(-1) avg_pos_feature = torch.nn.functional.normalize(avg_pos_feature, dim=1) avg_neg_feature = torch.nn.functional.normalize(avg_neg_feature, dim=1) #avg lang feature no normalize??? pos_features = mask_features[[i]][target_reshape[[i]]==1].view(1, C, -1) neg_features = mask_features[[i]][target_reshape[[i]]==0].view(1, C, -1) #inter_neg_features = mask_features[[B-i-1]][target_reshape[[B-i-1]]==1].view(1, C, -1) #neg_features = torch.cat([intra_neg_features, inter_neg_features], dim=2) pos_features = torch.nn.functional.normalize(pos_features, dim=1) neg_features = torch.nn.functional.normalize(neg_features, dim=1) #print(avg_lang_feature.shape, avg_lang_feature[[i]].shape, pos_features.shape) lang_pos_scores = torch.einsum("bq,bqn->bn", avg_lang_feature[[i]], pos_features) lang_neg_scores = torch.einsum("bq,bqn->bn", avg_lang_feature[[i]], neg_features) lang_matrix = torch.cat([lang_pos_scores.unsqueeze(-1), lang_neg_scores.unsqueeze(1).repeat(1, lang_pos_scores.shape[1], 1)], dim=2) lang_labels = torch.zeros(lang_matrix.shape[1], dtype=torch.long).cuda() lang_labels = lang_labels.unsqueeze(0).repeat(lang_matrix.shape[0], 1) lang_score = torch.softmax(lang_matrix, -1) lang_score = 1.0 - lang_score[:, :, 0] pos_pos_scores = torch.einsum("bq,bqn->bn", avg_pos_feature, pos_features) pos_neg_scores = torch.einsum("bqn,bqm->bnm", pos_features, neg_features) pos_matrix = torch.cat([pos_pos_scores.unsqueeze(-1), pos_neg_scores], dim=2) pos_labels = torch.zeros(pos_matrix.shape[1], dtype=torch.long).cuda() pos_labels = pos_labels.unsqueeze(0).repeat(pos_matrix.shape[0], 1) pos_score = torch.softmax(pos_matrix, -1) pos_score = 1.0 - pos_score[:, :, 0] #pos_weight = pos_weight.view(-1, pos_weight.shape[-1]) #intra_neg_features = torch.nn.functional.normalize(intra_neg_features, dim=1) neg_neg_scores = torch.einsum("bq,bqn->bn", avg_neg_feature, neg_features) neg_pos_scores = torch.einsum("bqn,bqm->bnm", neg_features, pos_features) neg_matrix = torch.cat([neg_neg_scores.unsqueeze(-1), neg_pos_scores], dim=2) neg_labels = torch.zeros(neg_matrix.shape[1], dtype=torch.long).cuda() neg_labels = neg_labels.unsqueeze(0).repeat(neg_matrix.shape[0], 1) neg_score = torch.softmax(neg_matrix, -1) neg_score = 1.0 - neg_score[:, :, 0] #neg_weight = neg_weight.view(-1, neg_weight.shape[-1]) pos_loss = (torch.pow(pos_score, args.plic_pos_alpha) * torch.nn.functional.cross_entropy(pos_matrix.view(-1, pos_matrix.shape[-1])/args.plic_pos_temp, pos_labels.view(-1), reduction='none')).mean() neg_loss = (torch.pow(neg_score, args.plic_neg_alpha) * torch.nn.functional.cross_entropy(neg_matrix.view(-1, neg_matrix.shape[-1])/args.plic_neg_temp, neg_labels.view(-1), reduction='none')).mean() lang_loss = (torch.pow(lang_score, args.plic_lang_alpha) * torch.nn.functional.cross_entropy(lang_matrix.view(-1, lang_matrix.shape[-1])/args.plic_lang_temp, lang_labels.view(-1), reduction='none')).mean() plic_pos_loss += pos_loss plic_neg_loss += neg_loss plic_lang_loss += lang_loss #cl_loss += 0.5 * (torch.nn.functional.cross_entropy(pos_matrix.view(-1, pos_matrix.shape[-1])/cl_temp, pos_labels.view(-1))+torch.nn.functional.cross_entropy(neg_matrix.view(-1, neg_matrix.shape[-1])/cl_temp, neg_labels.view(-1))) plic_pos_loss = (args.plic_pos_weight * plic_pos_loss) / B plic_neg_loss = (args.plic_neg_weight * plic_neg_loss) / B plic_lang_loss = (args.plic_lang_weight * plic_lang_loss) / B plic_loss = plic_pos_loss + plic_neg_loss +plic_lang_loss #print(output.device, target.device) losses = criterion(output, target) weight_dict = criterion.weight_dict loss_ce = 0.0 loss_dice = 0.0 loss_mask = 0.0 for k in list(losses.keys()): if k in weight_dict: losses[k] *= criterion.weight_dict[k] if '_ce' in k: loss_ce += losses[k] elif '_dice' in k: loss_dice += losses[k] else: loss_mask += losses[k] else: # remove this loss if not specified in `weight_dict` losses.pop(k) #loss = 0.3 * loss_ce + 0.3 * loss_dice + 0.4 * loss_mask smlm_loss = args.smlm_weight * nn.CrossEntropyLoss(ignore_index=-1)(mlm_predictions, mlm_targets) loss = loss_ce + loss_dice + loss_mask + plic_loss + smlm_loss #loss = criterion(output.squeeze(1), target.float()) 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=loss.item(), lr=optimizer.param_groups[0]["lr"], loss_ce=loss_ce.item(), loss_dice=loss_dice.item(), loss_mask=loss_mask.item(), plic_loss=plic_loss.item(), plic_lang_loss=plic_lang_loss.item(), plic_pos_loss=plic_pos_loss.item(), plic_neg_loss=plic_neg_loss.item(), smlm_loss=smlm_loss.item()) #metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"], loss_ce=loss_ce.item(), loss_dice=loss_dice.item(), loss_mask=loss_mask.item(), cl_loss=cl_loss.item(), cl_lang_loss=cl_lang_loss_print, cl_pos_loss=cl_pos_loss_print, cl_neg_loss=cl_neg_loss_print) #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() #del loss #del cl_loss #del cl_lang_loss #del loss_ce #del loss_dice #del loss_mask torch.cuda.synchronize() def main(args): #def main(local_rank, args): #ip = os.environ['MASTER_IP'] #port = os.environ['MASTER_PORT'] #hosts = int(os.environ['WORLD_SIZE']) # 机器个数 1 #rank = int(os.environ['RANK']) # 当前机器编号 #gpus = torch.cuda.device_count() # 每台机器的GPU个数 #print(local_rank, rank, gpus) #3 0 8 #dist.init_process_group(backend='nccl', init_method=f'tcp://{ip}:{port}', world_size=hosts*gpus, rank=rank*gpus+local_rank) #torch.cuda.set_device(local_rank) #dist.barrier() ##utils.init_distributed_mode(args) #args.distributed=True #args.gpu = local_rank #print(args) ##misc.init_distributed_mode(args) #print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) #print("{}".format(args).replace(', ', ',\n')) #device = torch.device(args.device) # fix the seed for reproducibility seed = args.seed + utils.get_rank() print('seed', seed) torch.manual_seed(seed) np.random.seed(seed) #cudnn.benchmark = True 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() #num_tasks = hosts*gpus #global_rank = rank*gpus+local_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=True, drop_last=True) data_loader_test = torch.utils.data.DataLoader( dataset_test, batch_size=1, sampler=test_sampler, pin_memory=True, num_workers=args.workers) # model initialization print(args.model) model = multimodal_segmentation_ppm.__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=[local_rank], find_unused_parameters=True) #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=False) #single_model = model.module if args.model != 'lavt_one': model_class = MultiModalBert bert_model = model_class.from_pretrained(args.ck_bert, embed_dim=model.backbone.embed_dim) 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=[local_rank]) #single_bert_model = bert_model.module else: bert_model = None single_bert_model = None input_shape = dict() input_shape['s1'] = Dict({'channel': 128, 'stride': 4}) input_shape['s2'] = Dict({'channel': 256, 'stride': 8}) input_shape['s3'] = Dict({'channel': 512, 'stride': 16}) input_shape['s4'] = Dict({'channel': 1024, 'stride': 32}) cfg = Dict() cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4 cfg.MODEL.MASK_FORMER.DROPOUT = 0.0 cfg.MODEL.MASK_FORMER.NHEADS = 8 cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = args.transformer_enc_layers cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM = 256 cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256 cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["s1", "s2", "s3", "s4"] cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 1 cfg.MODEL.MASK_FORMER.HIDDEN_DIM = 256 cfg.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES = args.num_object_queries cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD = args.dim_feedforward cfg.MODEL.MASK_FORMER.DEC_LAYERS = args.dec_layers cfg.MODEL.MASK_FORMER.PRE_NORM = False cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION = True cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT = args.no_object_weight cfg.MODEL.MASK_FORMER.CLASS_WEIGHT = args.class_weight cfg.MODEL.MASK_FORMER.DICE_WEIGHT = args.dice_weight cfg.MODEL.MASK_FORMER.MASK_WEIGHT = args.mask_weight cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS = args.train_num_points cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO = 3.0 cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75 print(cfg) maskformer_head = MaskFormerHead(cfg, input_shape) maskformer_head = torch.nn.SyncBatchNorm.convert_sync_batchnorm(maskformer_head) #maskformer_head.cuda() #maskformer_head = torch.nn.parallel.DistributedDataParallel(maskformer_head, device_ids=[args.local_rank], find_unused_parameters=False) #single_head = maskformer_head.module #print(single_head) model = WrapperModel(model.backbone, bert_model, maskformer_head, args) model.cuda() model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], find_unused_parameters=True) single_model = model.module # mask2former loss deep_supervision = cfg.MODEL.MASK_FORMER.DEEP_SUPERVISION no_object_weight = cfg.MODEL.MASK_FORMER.NO_OBJECT_WEIGHT # loss weights class_weight = cfg.MODEL.MASK_FORMER.CLASS_WEIGHT dice_weight = cfg.MODEL.MASK_FORMER.DICE_WEIGHT mask_weight = cfg.MODEL.MASK_FORMER.MASK_WEIGHT # self.criterion = Criterion(self.num_classes) # building criterion matcher = HungarianMatcher( cost_class=class_weight, cost_mask=mask_weight, cost_dice=dice_weight, num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, ) weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight} if deep_supervision: dec_layers = cfg.MODEL.MASK_FORMER.DEC_LAYERS aux_weight_dict = {} for i in range(dec_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) losses = ["labels", "masks"] criterion = SetCriterion( cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES, matcher=matcher, weight_dict=weight_dict, eos_coef=no_object_weight, losses=losses, num_points=cfg.MODEL.MASK_FORMER.TRAIN_NUM_POINTS, oversample_ratio=cfg.MODEL.MASK_FORMER.OVERSAMPLE_RATIO, importance_sample_ratio=cfg.MODEL.MASK_FORMER.IMPORTANCE_SAMPLE_RATIO, device='cuda' ) if args.resume == "auto": last_ckpt = "" for e in range(args.epochs): ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth') if os.path.exists(ckpt_path): last_ckpt = ckpt_path args.resume = last_ckpt # 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.image_model.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]}, # the following are the parameters of bert {"params": reduce(operator.concat, [[p for p in single_model.language_model.encoder.layer[i].parameters() if p.requires_grad] for i in range(10)])}, {"params": single_model.language_model.pwams.parameters()}, {"params": single_model.language_model.res_gates.parameters()}, {"params": single_model.language_model.norms.parameters()}, {"params": single_model.lang_proj.parameters()}, #{"params": single_model.language_model.parameters()}, {'params': single_model.mlm_head.parameters()}, {'params': single_model.mlm_vis_proj.parameters()}, {'params': single_model.mlm_lang_proj.parameters()}, {'params': single_model.mlm_transformer.parameters()}, {'params': single_model.mlm_pos_embeds.parameters()}, {'params': single_model.mlm_modal_embeds.parameters()}, {'params': single_model.mlm_mask_embed.parameters()}, {'params': single_model.mlm_pos_mlp.parameters()}, #{'params': mlm_head.parameters(), 'weight_decay': 0.0}, #{'params': mlm_vis_proj.parameters(), 'weight_decay': 0.0}, #{'params': mlm_lang_proj.parameters(), 'weight_decay': 0.0}, #{'params': mlm_transformer.parameters(), 'weight_decay': 0.0}, #{'params': mlm_pos_embeds.parameters(), 'weight_decay': 0.0}, #{'params': mlm_modal_embeds.parameters(), 'weight_decay': 0.0}, #{'params': mlm_mask_embed.parameters(), 'weight_decay': 0.0}, #{'params': mlm_pos_mlp.parameters(), 'weight_decay': 0.0}, ] # 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): data_loader.sampler.set_epoch(epoch) train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, epoch, args.print_freq, iterations, args) iou, overallIoU = evaluate(model, data_loader_test) print('Average object IoU {}'.format(iou)) print('Overall IoU {}'.format(overallIoU)) dict_to_save = {'model': single_model.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args, 'lr_scheduler': lr_scheduler.state_dict()} checkpoint_path = os.path.join(args.output_dir, 'checkpoint-{}.pth'.format(epoch)) utils.save_on_master(dict_to_save, str(checkpoint_path) + '_TEMP') if utils.is_main_process(): os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path)) if utils.is_main_process(): ckpt_paths = [] for e in range(args.epochs): ckpt_path = os.path.join(args.output_dir, f'checkpoint-{e}.pth') print(ckpt_path) if os.path.exists(ckpt_path): ckpt_paths.append(ckpt_path) print(ckpt_paths) for ckpt_path in ckpt_paths[:-args.max_ckpt]: os.remove(ckpt_path) print("remove {:s}".format(ckpt_path)) save_checkpoint = (best_oIoU < overallIoU) if save_checkpoint: print('Better epoch: {}\n'.format(epoch)) dict_to_save = {'model': single_model.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epoch, 'args': args, 'lr_scheduler': lr_scheduler.state_dict()} checkpoint_path = os.path.join(args.output_dir, 'model_best_{}.pth'.format(args.model_id)) utils.save_on_master(dict_to_save, checkpoint_path + '_TEMP') if utils.is_main_process(): os.rename(str(checkpoint_path) + '_TEMP', str(checkpoint_path)) best_oIoU = overallIoU torch.cuda.empty_cache() # 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() os.makedirs(args.output_dir, exist_ok=True) # set up distributed learning utils.init_distributed_mode(args) print('Image size: {}'.format(str(args.img_size))) main(args) #mp.spawn(main, args=(args,), nprocs=torch.cuda.device_count())