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km_swin_t_config.py
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# PATH TO PRETRAINED CHECKPOINT
pretrained_ckpt_path = None
dataset_type = 'CocoDataset'
gpu_ids = range(1)
seed = 42
device = 'cuda'
work_dir = './my_experiment'
# data_root ONLY REQUIRED WHILE TRAINING
# Specify Training Data root directory (data in COCO format) here
data_root = '<PATH TO COCO TRAINING DATASET DIRECTORY>'
image_size = (512, 512)
classes = ('line', )
num_things_classes = len(classes)
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline_LineEX = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True,
with_seg=False),
dict(
type='RandomFlip',
flip_ratio=[0.3, 0.3],
direction=['horizontal', 'vertical']),
# dict(type='RandomShift', shift_ratio=0.3, max_shift_px=min(image_size)//10),
# dict(
# type='RandomCrop',
# crop_ratio=0.3,
# crop_size=(int(0.85*image_size[0]), int(0.85*image_size[1])),
# crop_type='absolute',
# recompute_bbox=False,
# allow_negative_crop=True),
dict(
type='Resize',
img_scale=[image_size],
ratio_range=None,
multiscale_mode='value',
keep_ratio=True),
dict(
type='Pad',
size=image_size,
pad_val=dict(img=(255, 255, 255), masks=0, seg=255)),
dict(
type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle', img_to_float=True),
dict(type='Collect', keys=['img', 'gt_labels', 'gt_bboxes', 'gt_masks'])
]
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True,
with_seg=False),
dict(
type='RandomFlip',
flip_ratio=[0.3, 0.3],
direction=['horizontal', 'vertical']),
dict(type='RandomShift', shift_ratio=0.3, max_shift_px=min(image_size)//10),
dict(
type='RandomCrop',
crop_ratio=0.3,
crop_size=(int(0.85*image_size[0]), int(0.85*image_size[1])),
crop_type='absolute',
recompute_bbox=False,
allow_negative_crop=True),
dict(
type='Resize',
img_scale=[image_size],
ratio_range=None,
multiscale_mode='value',
keep_ratio=True),
dict(
type='Pad',
size=image_size,
pad_val=dict(img=(255, 255, 255), masks=0, seg=255)),
dict(
type='Normalize', **img_norm_cfg),
dict(type='DefaultFormatBundle', img_to_float=True),
dict(type='Collect', keys=['img', 'gt_labels', 'gt_bboxes', 'gt_masks'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=image_size,
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.0),
dict(
type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
PMC_test_data = dict(
type='CocoDataset',
classes=classes,
ann_file=data_root + 'coco_pmc_unclean/annotations/val_coco_annot.json',
img_prefix=data_root + 'coco_pmc_unclean/val_images',
pipeline=test_pipeline)
FineTuneTrainDataset = dict(
type='CocoDataset',
classes=classes,
ann_file='dataset/train/coco_annot.json',
img_prefix='dataset',
pipeline=train_pipeline
)
FineTuneTestDataset = dict(
type='CocoDataset',
classes=classes,
ann_file='dataset/test/coco_annot.json',
img_prefix='dataset',
pipeline=test_pipeline
)
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=[
dict(
type='RepeatDataset',
times=1,
dataset=FineTuneTrainDataset # 1500
)
],
test=FineTuneTestDataset)
# Validation config same as test (necessary for mmdetection to run)
data['val'] = data['test']
#data['train']['datasets'].pop(1)
evaluation = dict(interval=250, metric=['segm'], save_best='segm_mAP')
checkpoint_config = dict(
interval=500, by_epoch=False, save_last=True, max_keep_ckpts=3)
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False)
])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = pretrained_ckpt_path
resume_from = None
workflow = [('train', 5000)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
model = dict(
type='Mask2Former',
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.3,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
frozen_stages=-1,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
)),
panoptic_head=dict(
type='Mask2FormerHead',
in_channels=[96, 192, 384, 768],
strides=[4, 8, 16, 32],
feat_channels=256,
out_channels=256,
num_points=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=1.0,
reduction='mean',
class_weight=[1.0, 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),
use_sigmoid=True),
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='MaskHungarianAssigner',
cls_cost=dict(type='ClassificationCost', weight=2.0),
mask_cost=dict(
type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
dice_cost=dict(
type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
sampler=dict(type='MaskPseudoSampler')),
test_cfg=dict(
panoptic_on=True,
semantic_on=False,
instance_on=True,
max_per_image=100,
iou_thr=0.8,
filter_low_score=True),
init_cfg=None)
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
optimizer = dict(
type='AdamW',
lr=1e-4,
weight_decay=0.05,
eps=1e-08,
betas=(0.9, 0.999),
paramwise_cfg=dict(
custom_keys=dict({
'backbone':
dict(lr_mult=0.2, decay_mult=1.0),
'query_embed':
dict(lr_mult=1.0, decay_mult=0.0),
'query_feat':
dict(lr_mult=1.0, decay_mult=0.0),
'level_embed':
dict(lr_mult=1.0, decay_mult=0.0),
'backbone.patch_embed.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'absolute_pos_embed':
dict(lr_mult=0.1, decay_mult=0.0),
'relative_position_bias_table':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.0.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.0.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.2.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.3.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.4.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.5.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.3.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.3.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.0.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0)
}),
norm_decay_mult=0.0))
optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
lr_config = dict(
policy='step',
gamma=0.75,
by_epoch=False,
step=5000,
warmup='linear',
warmup_by_epoch=False,
warmup_ratio=1.0,
warmup_iters=10)
max_iters = 100000
runner = dict(type='IterBasedRunner', max_iters=100000)
interval = 5000
ckpt_interval = 500
dynamic_intervals = [(100001, 100000)]
pad_cfg = dict(img=(255, 255, 255), masks=0, seg=255)
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
depths = [2, 2, 6, 2]
backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
custom_keys = dict({
'backbone':
dict(lr_mult=0.1, decay_mult=1.0),
'backbone.patch_embed.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'absolute_pos_embed':
dict(lr_mult=0.1, decay_mult=0.0),
'relative_position_bias_table':
dict(lr_mult=0.1, decay_mult=0.0),
'query_embed':
dict(lr_mult=1.0, decay_mult=0.0),
'query_feat':
dict(lr_mult=1.0, decay_mult=0.0),
'level_embed':
dict(lr_mult=1.0, decay_mult=0.0),
'backbone.stages.0.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.0.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.2.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.3.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.4.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.blocks.5.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.3.blocks.0.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.3.blocks.1.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.0.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.1.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0),
'backbone.stages.2.downsample.norm':
dict(lr_mult=0.1, decay_mult=0.0)
})