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mmdet/configs/deformable_detr/deformable_detr_r50_16xb2_50e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_detection import *
from .._base_.default_runtime import *
from mmcv.transforms import LoadImageFromFile, RandomChoice, RandomChoiceResize
from mmengine.optim.optimizer import OptimWrapper
from mmengine.optim.scheduler import MultiStepLR
from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
from torch.optim.adamw import AdamW
from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
RandomCrop, RandomFlip, Resize)
from mmdet.models.backbones import ResNet
from mmdet.models.data_preprocessors import DetDataPreprocessor
from mmdet.models.dense_heads import DeformableDETRHead
from mmdet.models.detectors import DeformableDETR
from mmdet.models.losses import FocalLoss, GIoULoss, L1Loss
from mmdet.models.necks import ChannelMapper
from mmdet.models.task_modules import (BBoxL1Cost, FocalLossCost,
HungarianAssigner, IoUCost)
model = dict(
type=DeformableDETR,
num_queries=300,
num_feature_levels=4,
with_box_refine=False,
as_two_stage=False,
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=1),
backbone=dict(
type=ResNet,
depth=50,
num_stages=4,
out_indices=(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')),
neck=dict(
type=ChannelMapper,
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
encoder=dict( # DeformableDetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=256,
batch_first=True),
ffn_cfg=dict(
embed_dims=256, feedforward_channels=1024, ffn_drop=0.1))),
decoder=dict( # DeformableDetrTransformerDecoder
num_layers=6,
return_intermediate=True,
layer_cfg=dict( # DeformableDetrTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
cross_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=256,
batch_first=True),
ffn_cfg=dict(
embed_dims=256, feedforward_channels=1024, ffn_drop=0.1)),
post_norm_cfg=None),
positional_encoding=dict(num_feats=128, normalize=True, offset=-0.5),
bbox_head=dict(
type=DeformableDETRHead,
num_classes=80,
sync_cls_avg_factor=True,
loss_cls=dict(
type=FocalLoss,
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type=L1Loss, loss_weight=5.0),
loss_iou=dict(type=GIoULoss, loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type=HungarianAssigner,
match_costs=[
dict(type=FocalLossCost, weight=2.0),
dict(type=BBoxL1Cost, weight=5.0, box_format='xywh'),
dict(type=IoUCost, iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=100))
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadAnnotations, with_bbox=True),
dict(type=RandomFlip, prob=0.5),
dict(
type=RandomChoice,
transforms=[
[
dict(
type=RandomChoiceResize,
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
resize_type=Resize,
keep_ratio=True)
],
[
dict(
type=RandomChoiceResize,
# The radio of all image in train dataset < 7
# follow the original implement
scales=[(400, 4200), (500, 4200), (600, 4200)],
resize_type=Resize,
keep_ratio=True),
dict(
type=RandomCrop,
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type=RandomChoiceResize,
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
resize_type=Resize,
keep_ratio=True)
]
]),
dict(type=PackDetInputs)
]
train_dataloader.update(
dict(
dataset=dict(
filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline)))
# optimizer
optim_wrapper = dict(
type=OptimWrapper,
optimizer=dict(type=AdamW, lr=0.0002, weight_decay=0.0001),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1),
'sampling_offsets': dict(lr_mult=0.1),
'reference_points': dict(lr_mult=0.1)
}))
# learning policy
max_epochs = 50
train_cfg = dict(
type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type=ValLoop)
test_cfg = dict(type=TestLoop)
param_scheduler = [
dict(
type=MultiStepLR,
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[40],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (16 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=32)
mmdet/configs/deformable_detr/deformable_detr_refine_r50_16xb2_50e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .deformable_detr_r50_16xb2_50e_coco import *
model.update(dict(with_box_refine=True))
mmdet/configs/deformable_detr/deformable_detr_refine_twostage_r50_16xb2_50e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .deformable_detr_refine_r50_16xb2_50e_coco import *
model.update(dict(as_two_stage=True))
mmdet/configs/detr/detr_r101_8xb2_500e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.model.weight_init import PretrainedInit
with read_base():
from .detr_r50_8xb2_500e_coco import *
model.update(
dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet101'))))
mmdet/configs/detr/detr_r18_8xb2_500e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.model.weight_init import PretrainedInit
with read_base():
from .detr_r50_8xb2_500e_coco import *
model.update(
dict(
backbone=dict(
depth=18,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[512])))
mmdet/configs/detr/detr_r50_8xb2_150e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms import RandomChoice, RandomChoiceResize
from mmcv.transforms.loading import LoadImageFromFile
from mmengine.config import read_base
from mmengine.model.weight_init import PretrainedInit
from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
from mmengine.optim.scheduler.lr_scheduler import MultiStepLR
from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
from torch.nn.modules.activation import ReLU
from torch.nn.modules.batchnorm import BatchNorm2d
from torch.optim.adamw import AdamW
from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
RandomCrop, RandomFlip, Resize)
from mmdet.models import (DETR, ChannelMapper, DetDataPreprocessor, DETRHead,
ResNet)
from mmdet.models.losses.cross_entropy_loss import CrossEntropyLoss
from mmdet.models.losses.iou_loss import GIoULoss
from mmdet.models.losses.smooth_l1_loss import L1Loss
from mmdet.models.task_modules import (BBoxL1Cost, ClassificationCost,
HungarianAssigner, IoUCost)
with read_base():
from .._base_.datasets.coco_detection import *
from .._base_.default_runtime import *
model = dict(
type=DETR,
num_queries=100,
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=1),
backbone=dict(
type=ResNet,
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet50')),
neck=dict(
type=ChannelMapper,
in_channels=[2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=None,
num_outs=1),
encoder=dict( # DetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type=ReLU, inplace=True)))),
decoder=dict( # DetrTransformerDecoder
num_layers=6,
layer_cfg=dict( # DetrTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
dropout=0.1,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type=ReLU, inplace=True))),
return_intermediate=True),
positional_encoding=dict(num_feats=128, normalize=True),
bbox_head=dict(
type=DETRHead,
num_classes=80,
embed_dims=256,
loss_cls=dict(
type=CrossEntropyLoss,
bg_cls_weight=0.1,
use_sigmoid=False,
loss_weight=1.0,
class_weight=1.0),
loss_bbox=dict(type=L1Loss, loss_weight=5.0),
loss_iou=dict(type=GIoULoss, loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type=HungarianAssigner,
match_costs=[
dict(type=ClassificationCost, weight=1.),
dict(type=BBoxL1Cost, weight=5.0, box_format='xywh'),
dict(type=IoUCost, iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=100))
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadAnnotations, with_bbox=True),
dict(type=RandomFlip, prob=0.5),
dict(
type=RandomChoice,
transforms=[[
dict(
type=RandomChoiceResize,
resize_type=Resize,
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type=RandomChoiceResize,
resize_type=Resize,
scales=[(400, 1333), (500, 1333), (600, 1333)],
keep_ratio=True),
dict(
type=RandomCrop,
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type=RandomChoiceResize,
resize_type=Resize,
scales=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
keep_ratio=True)
]]),
dict(type=PackDetInputs)
]
train_dataloader.update(dataset=dict(pipeline=train_pipeline))
# optimizer
optim_wrapper = dict(
type=OptimWrapper,
optimizer=dict(type=AdamW, lr=0.0001, weight_decay=0.0001),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
# learning policy
max_epochs = 150
train_cfg = dict(
type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type=ValLoop)
test_cfg = dict(type=TestLoop)
param_scheduler = [
dict(
type=MultiStepLR,
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[100],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
mmdet/configs/detr/detr_r50_8xb2_500e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.optim.scheduler.lr_scheduler import MultiStepLR
from mmengine.runner.loops import EpochBasedTrainLoop
with read_base():
from .detr_r50_8xb2_150e_coco import *
# learning policy
max_epochs = 500
train_cfg.update(
type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=10)
param_scheduler = [
dict(
type=MultiStepLR,
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[334],
gamma=0.1)
]
# only keep latest 2 checkpoints
default_hooks.update(checkpoint=dict(max_keep_ckpts=2))
mmdet/configs/dino/dino_4scale_r50_8xb2_12e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms import RandomChoice, RandomChoiceResize
from mmcv.transforms.loading import LoadImageFromFile
from mmengine.config import read_base
from mmengine.model.weight_init import PretrainedInit
from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
from mmengine.optim.scheduler.lr_scheduler import MultiStepLR
from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
from torch.nn.modules.batchnorm import BatchNorm2d
from torch.nn.modules.normalization import GroupNorm
from torch.optim.adamw import AdamW
from mmdet.datasets.transforms import (LoadAnnotations, PackDetInputs,
RandomCrop, RandomFlip, Resize)
from mmdet.models import (DINO, ChannelMapper, DetDataPreprocessor, DINOHead,
ResNet)
from mmdet.models.losses.focal_loss import FocalLoss
from mmdet.models.losses.iou_loss import GIoULoss
from mmdet.models.losses.smooth_l1_loss import L1Loss
from mmdet.models.task_modules import (BBoxL1Cost, FocalLossCost,
HungarianAssigner, IoUCost)
with read_base():
from .._base_.datasets.coco_detection import *
from .._base_.default_runtime import *
model = dict(
type=DINO,
num_queries=900, # num_matching_queries
with_box_refine=True,
as_two_stage=True,
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=1),
backbone=dict(
type=ResNet,
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type=BatchNorm2d, requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet50')),
neck=dict(
type=ChannelMapper,
in_channels=[512, 1024, 2048],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type=GroupNorm, num_groups=32),
num_outs=4),
encoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0))), # 0.1 for DeformDETR
decoder=dict(
num_layers=6,
return_intermediate=True,
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_heads=8,
dropout=0.0), # 0.1 for DeformDETR
cross_attn_cfg=dict(embed_dims=256, num_levels=4,
dropout=0.0), # 0.1 for DeformDETR
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048, # 1024 for DeformDETR
ffn_drop=0.0)), # 0.1 for DeformDETR
post_norm_cfg=None),
positional_encoding=dict(
num_feats=128,
normalize=True,
offset=0.0, # -0.5 for DeformDETR
temperature=20), # 10000 for DeformDETR
bbox_head=dict(
type=DINOHead,
num_classes=80,
sync_cls_avg_factor=True,
loss_cls=dict(
type=FocalLoss,
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0), # 2.0 in DeformDETR
loss_bbox=dict(type=L1Loss, loss_weight=5.0),
loss_iou=dict(type=GIoULoss, loss_weight=2.0)),
dn_cfg=dict( # TODO: Move to model.train_cfg ?
label_noise_scale=0.5,
box_noise_scale=1.0, # 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None,
num_dn_queries=100)), # TODO: half num_dn_queries
# training and testing settings
train_cfg=dict(
assigner=dict(
type=HungarianAssigner,
match_costs=[
dict(type=FocalLossCost, weight=2.0),
dict(type=BBoxL1Cost, weight=5.0, box_format='xywh'),
dict(type=IoUCost, iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=300)) # 100 for DeformDETR
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type=LoadImageFromFile, backend_args=backend_args),
dict(type=LoadAnnotations, with_bbox=True),
dict(type=RandomFlip, prob=0.5),
dict(
type=RandomChoice,
transforms=[
[
dict(
type=RandomChoiceResize,
resize_type=Resize,
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type=RandomChoiceResize,
resize_type=Resize,
# The radio of all image in train dataset < 7
# follow the original implement
scales=[(400, 4200), (500, 4200), (600, 4200)],
keep_ratio=True),
dict(
type=RandomCrop,
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type=RandomChoiceResize,
resize_type=Resize,
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
]
]),
dict(type=PackDetInputs)
]
train_dataloader.update(
dataset=dict(
filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline))
# optimizer
optim_wrapper = dict(
type=OptimWrapper,
optimizer=dict(
type=AdamW,
lr=0.0001, # 0.0002 for DeformDETR
weight_decay=0.0001),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)})
) # custom_keys contains sampling_offsets and reference_points in DeformDETR # noqa
# learning policy
max_epochs = 12
train_cfg = dict(
type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1)
val_cfg = dict(type=ValLoop)
test_cfg = dict(type=TestLoop)
param_scheduler = [
dict(
type=MultiStepLR,
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[11],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
mmdet/configs/dino/dino_4scale_r50_8xb2_24e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.runner.loops import EpochBasedTrainLoop
with read_base():
from .dino_4scale_r50_8xb2_12e_coco import *
max_epochs = 24
train_cfg.update(
dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
param_scheduler[0].update(dict(milestones=[20]))
mmdet/configs/dino/dino_4scale_r50_8xb2_36e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.runner.loops import EpochBasedTrainLoop
with read_base():
from .dino_4scale_r50_8xb2_12e_coco import *
max_epochs = 36
train_cfg.update(
dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
param_scheduler[0].update(dict(milestones=[30]))
mmdet/configs/dino/dino_4scale_r50_improved_8xb2_12e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
with read_base():
from .dino_4scale_r50_8xb2_12e_coco import *
# from deformable detr hyper
model.update(
dict(
backbone=dict(frozen_stages=-1),
bbox_head=dict(loss_cls=dict(loss_weight=2.0)),
positional_encoding=dict(offset=-0.5, temperature=10000),
dn_cfg=dict(group_cfg=dict(num_dn_queries=300))))
# optimizer
optim_wrapper.update(
dict(
optimizer=dict(lr=0.0002),
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1),
'sampling_offsets': dict(lr_mult=0.1),
'reference_points': dict(lr_mult=0.1)
})))
mmdet/configs/dino/dino_5scale_swin_l_8xb2_12e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.model.weight_init import PretrainedInit
from mmdet.models import SwinTransformer
with read_base():
from .dino_4scale_r50_8xb2_12e_coco import *
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 5
model.merge(
dict(
num_feature_levels=num_levels,
backbone=dict(
_delete_=True,
type=SwinTransformer,
pretrain_img_size=384,
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
# Please only add indices that would be used
# in FPN, otherwise some parameter will not be used
with_cp=True,
convert_weights=True,
init_cfg=dict(type=PretrainedInit, checkpoint=pretrained)),
neck=dict(in_channels=[192, 384, 768, 1536], num_outs=num_levels),
encoder=dict(
layer_cfg=dict(self_attn_cfg=dict(num_levels=num_levels))),
decoder=dict(
layer_cfg=dict(cross_attn_cfg=dict(num_levels=num_levels)))))
mmdet/configs/dino/dino_5scale_swin_l_8xb2_36e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base
from mmengine.runner.loops import EpochBasedTrainLoop
with read_base():
from .dino_5scale_swin_l_8xb2_12e_coco import *
max_epochs = 36
train_cfg.update(
dict(type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=1))
param_scheduler[0].update(dict(milestones=[27, 33]))
mmdet/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.datasets.coco_detection import *
from .._base_.default_runtime import *
from .._base_.models.faster_rcnn_r50_fpn import *
from .._base_.schedules.schedule_1x import *
mmdet/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_poly_1x_coco import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
mmdet/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_ms_poly_3x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from ..common.ms_poly_3x_coco_instance import *
from .._base_.models.mask_rcnn_r50_fpn import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type=PretrainedInit,
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
mmdet/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .._base_.models.mask_rcnn_r50_fpn import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet101')))
mmdet/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r50_fpn_2x_coco import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet101')))
mmdet/configs/mask_rcnn/mask_rcnn_r101_fpn_8xb8_amp_lsj_200e_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from .mask_rcnn_r18_fpn_8xb8_amp_lsj_200e_coco import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet101')))
mmdet/configs/mask_rcnn/mask_rcnn_r101_fpn_ms_poly_3x_coco.py 0 → 100644
View file @ a8562a56
# Copyright (c) OpenMMLab. All rights reserved.
# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
# mmcv >= 2.0.1
# mmengine >= 0.8.0
from mmengine.config import read_base
with read_base():
from ..common.ms_poly_3x_coco_instance import *
from .._base_.models.mask_rcnn_r50_fpn import *
from mmengine.model.weight_init import PretrainedInit
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type=PretrainedInit, checkpoint='torchvision://resnet101')))
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