Spaces:
Runtime error
Runtime error
File size: 7,034 Bytes
3b96cb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
# 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)
|