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())