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config.py
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config.py
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auto_scale_lr = dict(base_batch_size=2, 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 = '/home/20009200061/MQ/PhenoBench_rotate_8/'
dataset_type = 'CocoPanopticDataset'
default_hooks = dict(
checkpoint=dict(
by_epoch=False,
interval=4000,
max_keep_ckpts=20,
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 = [
(
112001,
112560,
),
]
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 = 4000
launcher = 'pytorch'
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_16xb1-lsj-100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_16xb1-lsj-100e_coco-panoptic_20220407_104949-82f8d28d.pth'
log_level = 'INFO'
log_processor = dict(by_epoch=False, type='LogProcessor', window_size=50)
max_iters = 112560
metainfo = dict(
classes=(
'crop',
'weed',
),
stuff_classes=('weed', ),
thing_classes=('crop', ))
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=192,
frozen_stages=-1,
init_cfg=dict(
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth',
type='Pretrained'),
mlp_ratio=4,
num_heads=[
6,
12,
24,
48,
],
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=1,
num_things_classes=1,
type='MaskFormerFusionHead'),
panoptic_head=dict(
enforce_decoder_input_project=False,
feat_channels=256,
in_channels=[
192,
384,
768,
1536,
],
loss_cls=dict(
class_weight=[
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=200,
num_stuff_classes=1,
num_things_classes=1,
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=0.1,
score_thr=0.93,
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 = 2
num_stuff_classes = 1
num_things_classes = 1
optim_wrapper = dict(
clip_grad=dict(max_norm=0.01, norm_type=2),
optimizer=dict(
betas=(
0.9,
0.999,
),
eps=1e-08,
lr=2.5e-05,
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=112560,
gamma=0.1,
milestones=[
90048,
106932,
],
type='MultiStepLR')
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'
resume = False
test_cfg = dict(type='TestLoop')
test_dataloader = dict(
batch_size=1,
dataset=dict(
ann_file='annotations/plant_instances_test.json',
backend_args=None,
data_prefix=dict(
img='test/images/'),
data_root='/data/PhenoBench',
metainfo=dict(
classes=(
'crop',
'weed',
),
stuff_classes=('weed', ),
thing_classes=('crop', )),
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(
format_only=True,
ann_file=
'/data/PhenoBench/plant_test_result/plant_instances_test.json',
backend_args=None,
outfile_prefix=
'/data/PhenoBench/plant_test_result/panoptic_plant_weed_test/',
type='CocoPanopticMetric'),
dict(
ann_file=
'/data/PhenoBench/plant_test_result/plant_instances_test.json',
backend_args=None,
format_only=True,
outfile_prefix='/data/PhenoBench/plant_test_result/out-result',
# metric=[
# 'segm',
# 'bbox',
# ],
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=[
(
112001,
112560,
),
],
max_iters=112560,
type='IterBasedTrainLoop',
val_interval=4000)
train_dataloader = dict(
batch_sampler=dict(type='AspectRatioBatchSampler'),
batch_size=1,
dataset=dict(
ann_file='annotations/panoptic_plant_weed_rotate_8_train.json',
backend_args=None,
data_prefix=dict(
img='train/images_rotate_8/',
seg='annotations/panoptic_plant_weed_rotate_8_train/'),
data_root='/home/20009200061/MQ/PhenoBench_rotate_8/',
filter_cfg=dict(filter_empty_gt=True, min_size=32),
metainfo=dict(
classes=(
'crop',
'weed',
),
stuff_classes=('weed', ),
thing_classes=('crop', )),
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=1,
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_plant_weed_val.json',
backend_args=None,
data_prefix=dict(
img='val/images/', seg='annotations/panoptic_plant_weed_val/'),
data_root='/home/20009200061/MQ/PhenoBench_rotate_8/',
metainfo=dict(
classes=(
'crop',
'weed',
),
stuff_classes=('weed', ),
thing_classes=('crop', )),
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=
'/home/20009200061/MQ/PhenoBench_rotate_8/annotations/panoptic_plant_weed_val.json',
backend_args=None,
seg_prefix=
'/home/20009200061/MQ/PhenoBench_rotate_8/annotations/panoptic_plant_weed_val/',
type='CocoPanopticMetric'),
dict(
ann_file=
'/home/20009200061/MQ/PhenoBench_rotate_8/annotations/singlecat_plant_instances_val.json',
backend_args=None,
metric=[
'segm',
'bbox',
],
type='CocoMetric'),
]
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
name='visualizer',
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
])
work_dir = '/home/20009200061/MQ/work_dir/mask2former_swin_l__coco_pre_plant_weed_rotate_8'