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imted_faster_rcnn_vit_base_2x_finetuning_10shot_coco.py
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imted_faster_rcnn_vit_base_2x_finetuning_10shot_coco.py
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_base_ = [
'../../_base_/datasets/coco_detection.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
pretrained = 'Path/of/Pre-trained/Weights'
load_from = 'Path/of/Base-Training/Weights'
norm_cfg = dict(type='LN', requires_grad=True)
model = dict(
type='imTED',
pretrained=pretrained,
backbone=dict(
type='VisionTransformer',
img_size=224,
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=True,
drop_path_rate=0.2,
learnable_pos_embed=True,
use_checkpoint=True,
with_simple_fpn=True,
last_feat=True),
neck=dict(
type='SimpleFPN',
in_channels=[768, 768, 768, 768],
out_channels=256,
norm_cfg=norm_cfg,
use_residual=False,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_skip_fpn=False,
with_mfm=True,
roi_head=dict(
type='imTEDRoIHead',
bbox_roi_extractor=[dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=768,
featmap_strides=[4, 8, 16, 32]),
dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=768,
featmap_strides=[16])],
bbox_head=dict(
type='MAEBBoxHead',
use_checkpoint=False,
in_channels=768,
img_size=224,
patch_size=16,
embed_dim=512,
depth=8,
num_heads=16,
mlp_ratio=4.,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
reg_decoded_bbox=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=10.0),
),
),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)
# soft-nms is also supported for rcnn testing
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
))
# augmentation strategy originates from DETR / Sparse RCNN
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='AutoAugment',
policies=[
[
dict(type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(type='Resize',
img_scale=[(400, 1333), (500, 1333), (600, 1333)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]
]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
evaluation = dict(interval=6, metric='bbox')
checkpoint_config = dict(interval=6)
classes = ('truck', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog',
'pizza', 'donut', 'cake', 'bed', 'toilet', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator',
'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'boat',
'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'bottle', 'chair', 'couch', 'potted plant', 'dining table', 'tv')
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
pipeline=train_pipeline,
classes = classes,
ann_file='path/of/annotations/json'),
val=dict(
classes = classes,
ann_file='path/of/annotations/json'),
test=dict(
classes = classes,
ann_file='path/of/annotations/json'))
# optimizer
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
constructor='LayerDecayOptimizerConstructorBackboneFronzen',
paramwise_cfg=dict(
num_encoder_layers=12,
num_decoder_layers=8,
layer_decay_rate=0.8,
)
)
# learning policy
lr_config = dict(policy='step', step=[81, 99])
runner = dict(type='EpochBasedRunnerAmp', max_epochs=108)
# do not use mmdet version fp16
fp16 = None
optimizer_config = dict(
type="DistOptimizerHook",
update_interval=1,
grad_clip=None,
coalesce=True,
bucket_size_mb=-1,
use_fp16=True,
)
# find_unused_parameters=True