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_base_ = [
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.SparseInst.sparseinst'], allow_failed_imports=False)
model = dict(
type='SparseInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=0,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
encoder=dict(
type='InstanceContextEncoder',
in_channels=[512, 1024, 2048],
out_channels=256),
decoder=dict(
type='BaseIAMDecoder',
in_channels=256 + 2,
num_classes=80,
ins_dim=256,
ins_conv=4,
mask_dim=256,
mask_conv=4,
kernel_dim=128,
scale_factor=2.0,
output_iam=False,
num_masks=100),
criterion=dict(
type='SparseInstCriterion',
num_classes=80,
assigner=dict(type='SparseInstMatcher', alpha=0.8, beta=0.2),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
alpha=0.25,
gamma=2.0,
reduction='sum',
loss_weight=2.0),
loss_obj=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=1.0),
loss_mask=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='DiceLoss',
use_sigmoid=True,
reduction='sum',
eps=5e-5,
loss_weight=2.0),
),
test_cfg=dict(score_thr=0.005, mask_thr_binary=0.45))
backend = 'pillow'
train_pipeline = [
dict(
type='LoadImageFromFile',
backend_args={{_base_.backend_args}},
imdecode_backend=backend),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='RandomChoiceResize',
scales=[(416, 853), (448, 853), (480, 853), (512, 853), (544, 853),
(576, 853), (608, 853), (640, 853)],
keep_ratio=True,
backend=backend),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(
type='LoadImageFromFile',
backend_args={{_base_.backend_args}},
imdecode_backend=backend),
dict(type='Resize', scale=(640, 853), keep_ratio=True, backend=backend),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=8,
num_workers=8,
sampler=dict(type='InfiniteSampler'),
dataset=dict(pipeline=train_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
val_dataloader = test_dataloader
val_evaluator = dict(metric='segm')
test_evaluator = val_evaluator
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(_delete_=True, type='AdamW', lr=0.00005, weight_decay=0.05))
train_cfg = dict(
_delete_=True,
type='IterBasedTrainLoop',
max_iters=270000,
val_interval=10000)
# learning rate
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=270000,
by_epoch=False,
milestones=[210000, 250000],
gamma=0.1)
]
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=10000, max_keep_ckpts=3))
log_processor = dict(by_epoch=False)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64, enable=True)
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