Spaces:
Runtime error
Runtime error
File size: 7,098 Bytes
412c852 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
_base_ = ['../_base_/default_runtime.py', '../_base_/datasets/ade20k.py']
custom_imports = dict(imports='mmdet.models', allow_failed_imports=False)
crop_size = (512, 512)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255,
size=crop_size,
test_cfg=dict(size_divisor=32))
num_classes = 150
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='ResNet',
depth=50,
deep_stem=False,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=False),
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
decode_head=dict(
type='Mask2FormerHead',
in_channels=[256, 512, 1024, 2048],
strides=[4, 8, 16, 32],
feat_channels=256,
out_channels=256,
num_classes=num_classes,
num_queries=100,
num_transformer_feat_level=3,
align_corners=False,
pixel_decoder=dict(
type='mmdet.MSDeformAttnPixelDecoder',
num_outs=3,
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU'),
encoder=dict( # DeformableDetrTransformerEncoder
num_layers=6,
layer_cfg=dict( # DeformableDetrTransformerEncoderLayer
self_attn_cfg=dict( # MultiScaleDeformableAttention
embed_dims=256,
num_heads=8,
num_levels=3,
num_points=4,
im2col_step=64,
dropout=0.0,
batch_first=True,
norm_cfg=None,
init_cfg=None),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True))),
init_cfg=None),
positional_encoding=dict( # SinePositionalEncoding
num_feats=128, normalize=True),
init_cfg=None),
enforce_decoder_input_project=False,
positional_encoding=dict( # SinePositionalEncoding
num_feats=128, normalize=True),
transformer_decoder=dict( # Mask2FormerTransformerDecoder
return_intermediate=True,
num_layers=9,
layer_cfg=dict( # Mask2FormerTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
attn_drop=0.0,
proj_drop=0.0,
dropout_layer=None,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.0,
dropout_layer=None,
add_identity=True)),
init_cfg=None),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=2.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=5.0),
loss_dice=dict(
type='mmdet.DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=5.0),
train_cfg=dict(
num_points=12544,
oversample_ratio=3.0,
importance_sample_ratio=0.75,
assigner=dict(
type='mmdet.HungarianAssigner',
match_costs=[
dict(type='mmdet.ClassificationCost', weight=2.0),
dict(
type='mmdet.CrossEntropyLossCost',
weight=5.0,
use_sigmoid=True),
dict(
type='mmdet.DiceCost',
weight=5.0,
pred_act=True,
eps=1.0)
]),
sampler=dict(type='mmdet.MaskPseudoSampler'))),
train_cfg=dict(),
test_cfg=dict(mode='whole'))
# dataset config
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(
type='RandomChoiceResize',
scales=[int(512 * x * 0.1) for x in range(5, 21)],
resize_type='ResizeShortestEdge',
max_size=2048),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackSegInputs')
]
train_dataloader = dict(batch_size=2, dataset=dict(pipeline=train_pipeline))
# optimizer
embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
optimizer = dict(
type='AdamW', lr=0.0001, weight_decay=0.05, eps=1e-8, betas=(0.9, 0.999))
optim_wrapper = dict(
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(
custom_keys={
'backbone': dict(lr_mult=0.1, decay_mult=1.0),
'query_embed': embed_multi,
'query_feat': embed_multi,
'level_embed': embed_multi,
},
norm_decay_mult=0.0))
# learning policy
param_scheduler = [
dict(
type='PolyLR',
eta_min=0,
power=0.9,
begin=0,
end=160000,
by_epoch=False)
]
# training schedule for 160k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=160000, val_interval=5000)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', by_epoch=False, interval=5000,
save_best='mIoU'),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='SegVisualizationHook'))
# Default setting for scaling LR automatically
# - `enable` means enable scaling LR automatically
# or not by default.
# - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=16)
|