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mask2former_swin-b.py
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auto_scale_lr = dict(base_batch_size=16, enable=False)
backbone_embed_multi = dict(decay_mult=0.0, lr_mult=0.1)
backbone_norm_multi = dict(decay_mult=0.0, lr_mult=0.1)
backend_args = None
batch_augments = [
dict(
img_pad_value=0,
mask_pad_value=0,
pad_mask=True,
pad_seg=True,
seg_pad_value=255,
size=(
1024,
1024,
),
type="BatchFixedSizePad",
),
]
custom_keys = dict(
{
"absolute_pos_embed": dict(decay_mult=0.0, lr_mult=0.1),
"backbone": dict(decay_mult=1.0, lr_mult=0.1),
"backbone.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.patch_embed.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.10.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.11.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.12.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.13.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.14.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.15.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.16.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.17.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.2.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.3.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.4.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.5.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.6.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.7.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.8.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.9.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.3.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.3.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"level_embed": dict(decay_mult=0.0, lr_mult=1.0),
"query_embed": dict(decay_mult=0.0, lr_mult=1.0),
"query_feat": dict(decay_mult=0.0, lr_mult=1.0),
"relative_position_bias_table": dict(decay_mult=0.0, lr_mult=0.1),
}
)
data_preprocessor = dict(
batch_augments=[
dict(
img_pad_value=0,
mask_pad_value=0,
pad_mask=True,
pad_seg=True,
seg_pad_value=255,
size=(
1024,
1024,
),
type="BatchFixedSizePad",
),
],
bgr_to_rgb=True,
mask_pad_value=0,
mean=[
123.675,
116.28,
103.53,
],
pad_mask=True,
pad_seg=True,
pad_size_divisor=32,
seg_pad_value=255,
std=[
58.395,
57.12,
57.375,
],
type="DetDataPreprocessor",
)
data_root = "data/coco/"
dataset_type = "CocoPanopticDataset"
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=5000, max_keep_ckpts=3, save_last=True, type="CheckpointHook"),
logger=dict(interval=50, type="LoggerHook"),
param_scheduler=dict(type="ParamSchedulerHook"),
sampler_seed=dict(type="DistSamplerSeedHook"),
timer=dict(type="IterTimerHook"),
visualization=dict(type="DetVisualizationHook"),
)
default_scope = "mmdet"
depths = [
2,
2,
18,
2,
]
dynamic_intervals = [
(
365001,
368750,
),
]
embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
env_cfg = dict(cudnn_benchmark=False, dist_cfg=dict(backend="nccl"), mp_cfg=dict(mp_start_method="fork", opencv_num_threads=0))
image_size = (
1024,
1024,
)
interval = 5000
load_from = None
log_level = "INFO"
log_processor = dict(by_epoch=False, type="LogProcessor", window_size=50)
max_iters = 368750
model = dict(
backbone=dict(
attn_drop_rate=0.0,
convert_weights=True,
depths=[
2,
2,
18,
2,
],
drop_path_rate=0.3,
drop_rate=0.0,
embed_dims=128,
frozen_stages=-1,
init_cfg=dict(
checkpoint="https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth",
type="Pretrained",
),
mlp_ratio=4,
num_heads=[
4,
8,
16,
32,
],
out_indices=(
0,
1,
2,
3,
),
patch_norm=True,
pretrain_img_size=384,
qk_scale=None,
qkv_bias=True,
type="SwinTransformer",
window_size=12,
with_cp=False,
),
data_preprocessor=dict(
batch_augments=[
dict(
img_pad_value=0,
mask_pad_value=0,
pad_mask=True,
pad_seg=True,
seg_pad_value=255,
size=(
1024,
1024,
),
type="BatchFixedSizePad",
),
],
bgr_to_rgb=True,
mask_pad_value=0,
mean=[
123.675,
116.28,
103.53,
],
pad_mask=True,
pad_seg=True,
pad_size_divisor=32,
seg_pad_value=255,
std=[
58.395,
57.12,
57.375,
],
type="DetDataPreprocessor",
),
init_cfg=None,
panoptic_fusion_head=dict(init_cfg=None, loss_panoptic=None, num_stuff_classes=53, num_things_classes=80, type="MaskFormerFusionHead"),
panoptic_head=dict(
enforce_decoder_input_project=False,
feat_channels=256,
in_channels=[
128,
256,
512,
1024,
],
loss_cls=dict(
class_weight=[
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
0.1,
],
loss_weight=2.0,
reduction="mean",
type="CrossEntropyLoss",
use_sigmoid=False,
),
loss_dice=dict(activate=True, eps=1.0, loss_weight=5.0, naive_dice=True, reduction="mean", type="DiceLoss", use_sigmoid=True),
loss_mask=dict(loss_weight=5.0, reduction="mean", type="CrossEntropyLoss", use_sigmoid=True),
num_queries=100,
num_stuff_classes=53,
num_things_classes=80,
num_transformer_feat_level=3,
out_channels=256,
pixel_decoder=dict(
act_cfg=dict(type="ReLU"),
encoder=dict(
layer_cfg=dict(
ffn_cfg=dict(
act_cfg=dict(inplace=True, type="ReLU"), embed_dims=256, feedforward_channels=1024, ffn_drop=0.0, num_fcs=2
),
self_attn_cfg=dict(batch_first=True, dropout=0.0, embed_dims=256, num_heads=8, num_levels=3, num_points=4),
),
num_layers=6,
),
norm_cfg=dict(num_groups=32, type="GN"),
num_outs=3,
positional_encoding=dict(normalize=True, num_feats=128),
type="MSDeformAttnPixelDecoder",
),
positional_encoding=dict(normalize=True, num_feats=128),
strides=[
4,
8,
16,
32,
],
transformer_decoder=dict(
init_cfg=None,
layer_cfg=dict(
cross_attn_cfg=dict(batch_first=True, dropout=0.0, embed_dims=256, num_heads=8),
ffn_cfg=dict(act_cfg=dict(inplace=True, type="ReLU"), embed_dims=256, feedforward_channels=2048, ffn_drop=0.0, num_fcs=2),
self_attn_cfg=dict(batch_first=True, dropout=0.0, embed_dims=256, num_heads=8),
),
num_layers=9,
return_intermediate=True,
),
type="Mask2FormerHead",
),
test_cfg=dict(filter_low_score=True, instance_on=True, iou_thr=0.8, max_per_image=100, panoptic_on=True, semantic_on=False),
train_cfg=dict(
assigner=dict(
match_costs=[
dict(type="ClassificationCost", weight=2.0),
dict(type="CrossEntropyLossCost", use_sigmoid=True, weight=5.0),
dict(eps=1.0, pred_act=True, type="DiceCost", weight=5.0),
],
type="HungarianAssigner",
),
importance_sample_ratio=0.75,
num_points=12544,
oversample_ratio=3.0,
sampler=dict(type="MaskPseudoSampler"),
),
type="Mask2Former",
)
num_classes = 133
num_stuff_classes = 53
num_things_classes = 80
optim_wrapper = dict(
clip_grad=dict(max_norm=0.01, norm_type=2),
optimizer=dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=0.0001,
type="AdamW",
weight_decay=0.05,
),
paramwise_cfg=dict(
custom_keys=dict(
{
"absolute_pos_embed": dict(decay_mult=0.0, lr_mult=0.1),
"backbone": dict(decay_mult=1.0, lr_mult=0.1),
"backbone.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.patch_embed.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.0.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.1.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.10.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.11.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.12.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.13.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.14.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.15.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.16.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.17.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.2.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.3.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.4.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.5.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.6.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.7.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.8.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.blocks.9.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.2.downsample.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.3.blocks.0.norm": dict(decay_mult=0.0, lr_mult=0.1),
"backbone.stages.3.blocks.1.norm": dict(decay_mult=0.0, lr_mult=0.1),
"level_embed": dict(decay_mult=0.0, lr_mult=1.0),
"query_embed": dict(decay_mult=0.0, lr_mult=1.0),
"query_feat": dict(decay_mult=0.0, lr_mult=1.0),
"relative_position_bias_table": dict(decay_mult=0.0, lr_mult=0.1),
}
),
norm_decay_mult=0.0,
),
type="OptimWrapper",
)
param_scheduler = dict(
begin=0,
by_epoch=False,
end=368750,
gamma=0.1,
milestones=[
327778,
355092,
],
type="MultiStepLR",
)
pretrained = "https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth"
resume = False
test_cfg = dict(type="TestLoop")
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file="annotations/panoptic_val2017.json",
backend_args=None,
data_prefix=dict(img="val2017/", seg="annotations/panoptic_val2017/"),
data_root="data/coco/",
pipeline=[
dict(backend_args=None, type="LoadImageFromFile"),
dict(
keep_ratio=True,
scale=(
1333,
800,
),
type="Resize",
),
dict(backend_args=None, type="LoadPanopticAnnotations"),
dict(
meta_keys=(
"img_id",
"img_path",
"ori_shape",
"img_shape",
"scale_factor",
),
type="PackDetInputs",
),
],
test_mode=True,
type="CocoPanopticDataset",
),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type="DefaultSampler"),
)
test_evaluator = [
dict(
ann_file="data/coco/annotations/panoptic_val2017.json",
backend_args=None,
seg_prefix="data/coco/annotations/panoptic_val2017/",
type="CocoPanopticMetric",
),
dict(
ann_file="data/coco/annotations/instances_val2017.json",
backend_args=None,
metric=[
"bbox",
"segm",
],
type="CocoMetric",
),
]
test_pipeline = [
dict(backend_args=None, type="LoadImageFromFile"),
dict(
keep_ratio=True,
scale=(
1333,
800,
),
type="Resize",
),
dict(backend_args=None, type="LoadPanopticAnnotations"),
dict(
meta_keys=(
"img_id",
"img_path",
"ori_shape",
"img_shape",
"scale_factor",
),
type="PackDetInputs",
),
]
train_cfg = dict(
dynamic_intervals=[
(
365001,
368750,
),
],
max_iters=368750,
type="IterBasedTrainLoop",
val_interval=5000,
)
train_dataloader = dict(
batch_sampler=dict(type="AspectRatioBatchSampler"),
batch_size=2,
dataset=dict(
ann_file="annotations/panoptic_train2017.json",
backend_args=None,
data_prefix=dict(img="train2017/", seg="annotations/panoptic_train2017/"),
data_root="data/coco/",
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(backend_args=None, to_float32=True, type="LoadImageFromFile"),
dict(backend_args=None, type="LoadPanopticAnnotations", with_bbox=True, with_mask=True, with_seg=True),
dict(prob=0.5, type="RandomFlip"),
dict(
keep_ratio=True,
ratio_range=(
0.1,
2.0,
),
scale=(
1024,
1024,
),
type="RandomResize",
),
dict(
allow_negative_crop=True,
crop_size=(
1024,
1024,
),
crop_type="absolute",
recompute_bbox=True,
type="RandomCrop",
),
dict(type="PackDetInputs"),
],
type="CocoPanopticDataset",
),
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=True, type="DefaultSampler"),
)
train_pipeline = [
dict(backend_args=None, to_float32=True, type="LoadImageFromFile"),
dict(backend_args=None, type="LoadPanopticAnnotations", with_bbox=True, with_mask=True, with_seg=True),
dict(prob=0.5, type="RandomFlip"),
dict(
keep_ratio=True,
ratio_range=(
0.1,
2.0,
),
scale=(
1024,
1024,
),
type="RandomResize",
),
dict(
allow_negative_crop=True,
crop_size=(
1024,
1024,
),
crop_type="absolute",
recompute_bbox=True,
type="RandomCrop",
),
dict(type="PackDetInputs"),
]
val_cfg = dict(type="ValLoop")
val_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file="annotations/panoptic_val2017.json",
backend_args=None,
data_prefix=dict(img="val2017/", seg="annotations/panoptic_val2017/"),
data_root="data/coco/",
pipeline=[
dict(backend_args=None, type="LoadImageFromFile"),
dict(
keep_ratio=True,
scale=(
1333,
800,
),
type="Resize",
),
dict(backend_args=None, type="LoadPanopticAnnotations"),
dict(
meta_keys=(
"img_id",
"img_path",
"ori_shape",
"img_shape",
"scale_factor",
),
type="PackDetInputs",
),
],
test_mode=True,
type="CocoPanopticDataset",
),
drop_last=False,
num_workers=2,
persistent_workers=True,
sampler=dict(shuffle=False, type="DefaultSampler"),
)
val_evaluator = [
dict(
ann_file="data/coco/annotations/panoptic_val2017.json",
backend_args=None,
seg_prefix="data/coco/annotations/panoptic_val2017/",
type="CocoPanopticMetric",
),
dict(
ann_file="data/coco/annotations/instances_val2017.json",
backend_args=None,
metric=[
"bbox",
"segm",
],
type="CocoMetric",
),
]
vis_backends = [
dict(type="LocalVisBackend"),
]
visualizer = dict(
name="visualizer",
type="DetLocalVisualizer",
vis_backends=[
dict(type="LocalVisBackend"),
],
)