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mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
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mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
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_base_ = [
'../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
seg_pad_value=255)
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
pad_mask=True,
mask_pad_value=0,
pad_seg=True,
seg_pad_value=255,
batch_augments=batch_augments)
num_things_classes = 80
num_stuff_classes = 53
num_classes = num_things_classes + num_stuff_classes
model = dict(
type='Mask2Former',
data_preprocessor=data_preprocessor,
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='torchvision://resnet50')),
panoptic_head=dict(
type='Mask2FormerHead',
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_things_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,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0)),
panoptic_fusion_head=dict(
type='MaskFormerFusionHead',
num_things_classes=num_things_classes,
num_stuff_classes=num_stuff_classes,
loss_panoptic=None,
init_cfg=None),
train_cfg=dict(
num_points=12544,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
assigner=dict(
type='HungarianAssigner',
match_costs=[
dict(type='ClassificationCost', weight=2.0),
dict(
type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
dict(type='DiceCost', weight=5.0, pred_act=True, eps=1.0)
]),
sampler=dict(type='MaskPseudoSampler')),
test_cfg=dict(
panoptic_on=True,
# For now, the dataset does not support
# evaluating semantic segmentation metric.
semantic_on=False,
instance_on=True,
# max_per_image is for instance segmentation.
max_per_image=100,
iou_thr=0.8,
# In Mask2Former's panoptic postprocessing,
# it will filter mask area where score is less than 0.5 .
filter_low_score=True),
init_cfg=None)
# dataset settings
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(type='RandomFlip', prob=0.5),
# large scale jittering
dict(
type='RandomResize',
scale=image_size,
ratio_range=(0.1, 2.0),
keep_ratio=True),
dict(
type='RandomCrop',
crop_size=image_size,
crop_type='absolute',
recompute_bbox=True,
allow_negative_crop=True),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_evaluator = [
dict(
type='CocoPanopticMetric',
ann_file=data_root + 'annotations/panoptic_val2017.json',
seg_prefix=data_root + 'annotations/panoptic_val2017/',
),
dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
)
]
test_evaluator = val_evaluator
# optimizer
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
optim_wrapper = dict(
type='OptimWrapper',
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),
clip_grad=dict(max_norm=0.01, norm_type=2))
# learning policy
max_iters = 368750
param_scheduler = dict(
type='MultiStepLR',
begin=0,
end=max_iters,
by_epoch=False,
milestones=[327778, 355092],
gamma=0.1)
# Before 365001th iteration, we do evaluation every 5000 iterations.
# After 365000th iteration, we do evaluation every 368750 iterations,
# which means that we do evaluation at the end of training.
interval = 5000
dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
train_cfg = dict(
type='IterBasedTrainLoop',
max_iters=max_iters,
val_interval=interval,
dynamic_intervals=dynamic_intervals)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
checkpoint=dict(
type='CheckpointHook',
by_epoch=False,
save_last=True,
max_keep_ckpts=3,
interval=interval))
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=False)
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)