queryinst_r50_fpn_1x_coco.py
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_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='QueryInst',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=0,
add_extra_convs='on_input',
num_outs=4),
rpn_head=dict(
type='EmbeddingRPNHead',
num_proposals=num_proposals,
proposal_feature_channel=256),
roi_head=dict(
type='SparseRoIHead',
num_stages=num_stages,
stage_loss_weights=[1] * num_stages,
proposal_feature_channel=256,
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='DIIHead',
num_classes=80,
num_ffn_fcs=2,
num_heads=8,
num_cls_fcs=1,
num_reg_fcs=3,
feedforward_channels=2048,
in_channels=256,
dropout=0.0,
ffn_act_cfg=dict(type='ReLU', inplace=True),
dynamic_conv_cfg=dict(
type='DynamicConv',
in_channels=256,
feat_channels=64,
out_channels=256,
input_feat_shape=7,
act_cfg=dict(type='ReLU', inplace=True),
norm_cfg=dict(type='LN')),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
clip_border=False,
target_means=[0., 0., 0., 0.],
target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
],
mask_head=[
dict(
type='DynamicMaskHead',
dynamic_conv_cfg=dict(
type='DynamicConv',
in_channels=256,
feat_channels=64,
out_channels=256,
input_feat_shape=14,
with_proj=False,
act_cfg=dict(type='ReLU', inplace=True),
norm_cfg=dict(type='LN')),
num_convs=4,
num_classes=80,
roi_feat_size=14,
in_channels=256,
conv_kernel_size=3,
conv_out_channels=256,
class_agnostic=False,
norm_cfg=dict(type='BN'),
upsample_cfg=dict(type='deconv', scale_factor=2),
loss_mask=dict(
type='DiceLoss',
loss_weight=8.0,
use_sigmoid=True,
activate=False,
eps=1e-5)) for _ in range(num_stages)
]),
# training and testing settings
train_cfg=dict(
rpn=None,
rcnn=[
dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBoxL1Cost', weight=5.0),
iou_cost=dict(type='IoUCost', iou_mode='giou',
weight=2.0)),
sampler=dict(type='PseudoSampler'),
pos_weight=1,
mask_size=28,
) for _ in range(num_stages)
]),
test_cfg=dict(
rpn=None, rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
# optimizer
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(policy='step', step=[8, 11], warmup_iters=1000)
runner = dict(type='EpochBasedRunner', max_epochs=12)