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box2mask_r50_lsj_8x2_50e_voc.py
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box2mask_r50_lsj_8x2_50e_voc.py
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_base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
num_thing_classes = 20
num_stuff_classes = 0
num_classes = num_thing_classes + num_stuff_classes
model = dict(
type='Box2Mask',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='https://download.pytorch.org/models/resnet50-11ad3fa6.pth')),
panoptic_head=dict(
type='Box2MaskHead',
in_channels=[256, 512, 1024, 2048], # pass to pixel_decoder inside
strides=[4, 8, 16, 32],
feat_channels=256,
out_channels=256,
num_things_classes=num_thing_classes,
num_stuff_classes=num_stuff_classes,
num_queries=100,
num_transformer_feat_level=3,
pixel_decoder=dict(
type='MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=256,
num_heads=8,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=False,
norm_cfg=None,
init_cfg=None),
ffn_cfgs=dict(
type='FFN',
embed_dims=256,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True)),
operation_order=('self_attn', 'norm', 'ffn', 'norm')),
init_cfg=None),
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=128, normalize=True),
init_cfg=None),
enforce_decoder_input_project=False,
positional_encoding=dict(
type='SinePositionalEncoding', num_feats=128, normalize=True),
transformer_decoder=dict(
type='DetrTransformerDecoder',
return_intermediate=True,
num_layers=9,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=False),
ffn_cfgs=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
add_identity=True),
feedforward_channels=2048,
operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
'ffn', 'norm')),
init_cfg=None),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0, ###original 2.0
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='LevelsetLoss',
loss_weight=1.0),
loss_box=dict(
type='BoxProjectionLoss',
loss_weight=5.0)),
panoptic_fusion_head=dict(
type='MaskFormerFusionHead',
num_things_classes=num_thing_classes,
num_stuff_classes=num_stuff_classes,
loss_panoptic=None,
init_cfg=None),
train_cfg=dict(
assigner=dict(
type='MaskHungarianAssigner',
cls_cost=dict(type='ClassificationCost', weight=2.0),
dice_cost=dict(type='BoxMatchingCost', weight=5.0, pred_act=True, eps=1.0)),
sampler=dict(type='MaskPseudoSampler')),
test_cfg=dict(
panoptic_on=False,
semantic_on=False,
instance_on=True,
max_per_image=100,
iou_thr=0.8,
filter_low_score=True),
init_cfg=None)
# dataset settings
image_size = (1024, 1024)
# image_size = (800, 800)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
pad_cfg = dict(img=(128, 128, 128), masks=0, seg=255)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=False),
dict(type='GenerateBoxMask'), #genenrate box mask
dict(type='RandomFlip', flip_ratio=0.5),
# large scale jittering
dict(
type='Resize',
img_scale=image_size,
ratio_range=(0.1, 2.0),
multiscale_mode='range',
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=image_size,
crop_type='absolute',
recompute_bbox=True,
allow_negative_crop=True),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-5, 1e-5), keep_empty=True),
dict(type='Pad', size=image_size, pad_val=pad_cfg),
dict(type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle', img_to_float=True),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Pad', size_divisor=32, pad_val=pad_cfg),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
dataset_type = 'PascalVOCDataset'
data_root = '/data/VOC_dataset/'
data = dict(
_delete_=True,
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc_2012_train.json',
img_prefix=data_root + 'train/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc_2012_val.json',
img_prefix=data_root + 'val/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc_2012_val.json',
img_prefix=data_root + 'val/',
pipeline=test_pipeline))
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
# optimizer
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.05,
eps=1e-8,
betas=(0.9, 0.999),
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1, decay_mult=1.0),
'query_embed': embed_multi,
'query_feat': embed_multi,
'level_embed': embed_multi,
},
norm_decay_mult=0.0))
optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
gamma=0.1,
by_epoch=False,
step=[29390, 31840],
warmup='linear',
warmup_by_epoch=False,
warmup_ratio=1.0, # no warmup
warmup_iters=10)
#50epoch
max_iters = 33065
runner = dict(type='IterBasedRunner', max_iters=max_iters)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False)
])
interval = 2000
workflow = [('train', interval)]
checkpoint_config = dict(
by_epoch=False, interval=interval, save_last=True, max_keep_ckpts=3)
dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
evaluation = dict(interval=interval, dynamic_intervals=dynamic_intervals, metric=['bbox', 'segm'])
find_unused_parameters = True
work_dir = './work_dirs/box2mask_voc_r50_50e/'