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litehrnet_s.yaml
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litehrnet_s.yaml
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model:
class_path: otx.algo.segmentation.litehrnet.OTXLiteHRNet
init_args:
label_info: 2
name_base_model: LiteHRNetS
criterion_configuration:
- type: CrossEntropyLoss
params:
ignore_index: 255
backbone_configuration:
norm_cfg:
type: BN
requires_grad: true
norm_eval: false
extra:
stem:
stem_channels: 32
out_channels: 32
expand_ratio: 1
strides:
- 2
- 2
extra_stride: true
input_norm: false
num_stages: 2
stages_spec:
num_modules:
- 4
- 4
num_branches:
- 2
- 3
num_blocks:
- 2
- 2
module_type:
- LITE
- LITE
with_fuse:
- true
- true
reduce_ratios:
- 8
- 8
num_channels:
- - 60
- 120
- - 60
- 120
- 240
out_modules:
conv:
enable: false
channels: 160
position_att:
enable: false
key_channels: 64
value_channels: 240
psp_size:
- 1
- 3
- 6
- 8
local_att:
enable: false
out_aggregator:
enable: false
add_input: false
pretrained_weights: "https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/models/custom_semantic_segmentation/litehrnetsv2_imagenet1k_rsc.pth"
decode_head_configuration:
norm_cfg:
type: BN
requires_grad: true
in_channels:
- 60
- 120
- 240
in_index:
- 0
- 1
- 2
input_transform: "multiple_select"
channels: 60
kernel_size: 1
num_convs: 1
concat_input: false
enable_aggregator: True
aggregator_merge_norm: None
aggregator_use_concat: False
dropout_ratio: -1
align_corners: false
optimizer:
class_path: torch.optim.Adam
init_args:
lr: 0.001
betas:
- 0.9
- 0.999
weight_decay: 0.0
scheduler:
class_path: otx.core.schedulers.LinearWarmupSchedulerCallable
init_args:
num_warmup_steps: 100
main_scheduler_callable:
class_path: lightning.pytorch.cli.ReduceLROnPlateau
init_args:
mode: max
factor: 0.1
patience: 4
monitor: val/Dice
engine:
task: SEMANTIC_SEGMENTATION
device: auto
callback_monitor: val/Dice
overrides:
callbacks:
- class_path: otx.algo.callbacks.adaptive_early_stopping.EarlyStoppingWithWarmup
init_args:
warmup_iters: 100
data: ../_base_/data/mmseg_base.yaml