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2025-07-07 11:27:50,676 - PropVG - INFO - dataset = 'MixedSeg'
data_root = './data/seqtr_type/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375])
train_pipeline = [
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle',
'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape',
'scale_factor', 'gt_ori_mask', 'target', 'empty',
'refer_target_index'
])
]
val_pipeline = [
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle',
'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape',
'scale_factor', 'gt_ori_mask', 'target', 'empty',
'refer_target_index'
])
]
test_pipeline = [
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file='data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask', 'gt_mask_rle',
'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape', 'pad_shape',
'scale_factor', 'gt_ori_mask', 'target', 'empty',
'refer_target_index'
])
]
word_emb_cfg = dict(type='GloVe')
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='MixedSeg',
which_set='train',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
val_refcoco_unc=dict(
type='MixedSeg',
which_set='val_refcoco_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
testA_refcoco_unc=dict(
type='MixedSeg',
which_set='testA_refcoco_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
testB_refcoco_unc=dict(
type='MixedSeg',
which_set='testB_refcoco_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
val_refcocoplus_unc=dict(
type='MixedSeg',
which_set='val_refcocoplus_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
testA_refcocoplus_unc=dict(
type='MixedSeg',
which_set='testA_refcocoplus_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
testB_refcocoplus_unc=dict(
type='MixedSeg',
which_set='testB_refcocoplus_unc',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
val_refcocog_umd=dict(
type='MixedSeg',
which_set='val_refcocog_umd',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')),
test_refcocog_umd=dict(
type='MixedSeg',
which_set='test_refcocog_umd',
img_source=['coco'],
annsfile=
'./data/seqtr_type/annotations/mixed-seg/instances_nogoogle_withid.json',
imgsfile='./data/seqtr_type/images/mscoco/train2014',
pipeline=[
dict(
type='LoadImageAnnotationsFromFile_TO',
max_token=20,
with_mask=True,
with_bbox=True,
dataset='MixedSeg',
use_token_type='beit3',
refer_file=
'data/seqtr_type/annotations/mixed-seg/coco_all.json',
object_area_filter=100,
object_area_rate_filter=[0.05, 0.8]),
dict(type='Resize', img_scale=(384, 384), keep_ratio=False),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375]),
dict(type='DefaultFormatBundle'),
dict(
type='CollectData',
keys=[
'img', 'ref_expr_inds', 'text_attention_mask',
'gt_mask_rle', 'gt_bbox'
],
meta_keys=[
'filename', 'expression', 'ori_shape', 'img_shape',
'pad_shape', 'scale_factor', 'gt_ori_mask', 'target',
'empty', 'refer_target_index'
])
],
word_emb_cfg=dict(type='GloVe')))
ema = False
ema_factor = 0.999
use_fp16 = False
seed = 6666
deterministic = True
log_level = 'INFO'
log_interval = 50
save_interval = -1
resume_from = None
load_from = 'work_dir/refcoco-mix/PropVG-refcoco-mix.pth'
finetune_from = None
evaluate_interval = 1
start_evaluate_epoch = 0
start_save_checkpoint = 20
max_token = 20
img_size = 384
patch_size = 16
model = dict(
type='MIXRefUniModel_OMG',
vis_enc=dict(
type='BEIT3',
img_size=384,
patch_size=16,
vit_type='base',
drop_path_rate=0.1,
vocab_size=64010,
freeze_layer=-1,
vision_embed_proj_interpolate=False,
pretrain='pretrain_weights/beit3_base_patch16_224.zip'),
lan_enc=None,
fusion=None,
head=dict(
type='REFHead',
input_channels=768,
hidden_channels=256,
num_queries=20,
detr_loss=dict(
criterion=dict(loss_class=1.0, loss_bbox=5.0, loss_giou=2.0),
matcher=dict(cost_class=1.0, cost_bbox=5.0, cost_giou=2.0)),
loss_weight=dict(
mask=dict(dice=1.0, bce=1.0, nt=0.2, neg=0),
bbox=0.1,
allbbox=0.1,
refer=1.0),
MTD=dict(K=100)),
post_params=dict(
score_weighted=False,
mask_threshold=0.5,
score_threshold=0.7,
with_nms=False,
with_mask=True),
process_visual=False,
visualize_params=dict(row_columns=(4, 5)),
visual_mode='test')
grad_norm_clip = 0.15
lr = 0.0005
optimizer_config = dict(
type='Adam',
lr=0.0005,
lr_vis_enc=5e-05,
lr_lan_enc=0.0005,
betas=(0.9, 0.98),
eps=1e-09,
weight_decay=0,
amsgrad=True)
scheduler_config = dict(
type='MultiStepLRWarmUp',
warmup_epochs=1,
decay_steps=[21, 27],
decay_ratio=0.1,
max_epoch=30)
launcher = 'pytorch'
distributed = True
rank = 0
world_size = 1
2025-07-07 11:27:58,403 - PropVG - INFO - Mixed-val_refcoco_unc size: 10834
2025-07-07 11:28:06,594 - PropVG - INFO - Mixed-testA_refcoco_unc size: 5657
2025-07-07 11:28:15,164 - PropVG - INFO - Mixed-testB_refcoco_unc size: 5095
2025-07-07 11:28:23,677 - PropVG - INFO - Mixed-val_refcocoplus_unc size: 10758
2025-07-07 11:28:30,907 - PropVG - INFO - Mixed-testA_refcocoplus_unc size: 5726
2025-07-07 11:28:38,494 - PropVG - INFO - Mixed-testB_refcocoplus_unc size: 4889
2025-07-07 11:28:49,090 - PropVG - INFO - Mixed-val_refcocog_umd size: 4896
2025-07-07 11:28:54,576 - PropVG - INFO - Mixed-test_refcocog_umd size: 9602
2025-07-07 11:29:02,664 - PropVG - INFO - loaded checkpoint from work_dir/refcoco-mix/PropVG-refcoco-mix.pth
2025-07-07 11:29:02,665 - PropVG - INFO - PropVG - evaluating set val_refcoco_unc
2025-07-07 11:32:39,213 - PropVG - INFO - ------------ validate ------------ time: 216.54, DetACC: 92.70, mIoU: 81.96, oIoU: 81.80, MaskACC@0.5-0.9: [92.24, 90.71, 87.59, 79.79, 46.59]DetACC@0.5-0.9: [92.70, 91.43, 88.90, 83.85, 66.30]
2025-07-07 11:32:43,474 - PropVG - INFO - PropVG - evaluating set testA_refcoco_unc
2025-07-07 11:34:47,838 - PropVG - INFO - ------------ validate ------------ time: 124.36, DetACC: 95.07, mIoU: 83.58, oIoU: 83.74, MaskACC@0.5-0.9: [94.56, 93.48, 90.93, 82.91, 46.61]DetACC@0.5-0.9: [95.07, 93.99, 92.17, 88.17, 69.29]
2025-07-07 11:34:53,297 - PropVG - INFO - PropVG - evaluating set testB_refcoco_unc
2025-07-07 11:36:51,290 - PropVG - INFO - ------------ validate ------------ time: 117.99, DetACC: 89.58, mIoU: 80.02, oIoU: 79.33, MaskACC@0.5-0.9: [89.19, 86.99, 83.45, 76.76, 51.07]DetACC@0.5-0.9: [89.58, 87.56, 84.61, 79.14, 61.83]
2025-07-07 11:36:56,652 - PropVG - INFO - PropVG - evaluating set val_refcocoplus_unc
2025-07-07 11:40:28,540 - PropVG - INFO - ------------ validate ------------ time: 211.88, DetACC: 87.27, mIoU: 77.14, oIoU: 74.81, MaskACC@0.5-0.9: [86.67, 85.36, 82.52, 75.28, 44.34]DetACC@0.5-0.9: [87.27, 86.30, 84.09, 79.64, 63.62]
2025-07-07 11:40:36,392 - PropVG - INFO - PropVG - evaluating set testA_refcocoplus_unc
2025-07-07 11:42:43,800 - PropVG - INFO - ------------ validate ------------ time: 127.40, DetACC: 90.87, mIoU: 79.83, oIoU: 78.72, MaskACC@0.5-0.9: [90.13, 88.79, 86.57, 79.46, 45.04]DetACC@0.5-0.9: [90.87, 89.82, 87.81, 83.92, 66.33]
2025-07-07 11:42:48,169 - PropVG - INFO - PropVG - evaluating set testB_refcocoplus_unc
2025-07-07 11:44:41,261 - PropVG - INFO - ------------ validate ------------ time: 113.09, DetACC: 81.26, mIoU: 72.18, oIoU: 69.15, MaskACC@0.5-0.9: [80.18, 78.20, 74.78, 68.68, 45.88]DetACC@0.5-0.9: [81.26, 79.40, 76.95, 72.20, 56.78]
2025-07-07 11:44:45,751 - PropVG - INFO - PropVG - evaluating set val_refcocog_umd
2025-07-07 11:46:42,173 - PropVG - INFO - ------------ validate ------------ time: 116.42, DetACC: 88.15, mIoU: 76.97, oIoU: 75.54, MaskACC@0.5-0.9: [86.17, 83.58, 79.43, 72.16, 44.87]DetACC@0.5-0.9: [88.15, 85.97, 82.90, 78.00, 63.09]
2025-07-07 11:46:46,257 - PropVG - INFO - PropVG - evaluating set test_refcocog_umd
2025-07-07 11:50:06,821 - PropVG - INFO - ------------ validate ------------ time: 200.56, DetACC: 88.30, mIoU: 77.72, oIoU: 77.40, MaskACC@0.5-0.9: [87.14, 85.01, 80.84, 72.78, 45.79]DetACC@0.5-0.9: [88.30, 86.71, 83.98, 79.07, 65.00]
2025-07-07 11:50:11,168 - PropVG - INFO - sucessfully save the results to work_dir/refcoco-mix/refer_output_thr0.7_no-nms_no-sw_0.5_100.xlsx !!!
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