deeplabv3plus_s8_dilated_resnet101.yaml

# employ os=8 to finetune after training on os=16

# system
seed: 1
mode: 0
distribute: True
num_parallel_workers: 8
device_target: "Ascend"
all_reduce_fusion_config: [90, 183, 279]

# dataset
dataset: "vocaug"
data_dir: "/path/to/vocaug0"
batch_size: 16
crop_size: 513
image_mean: [103.53, 116.28, 123.675]
image_std: [57.375, 57.120, 58.395]
max_scale: 2.0
min_scale: 0.5
ignore_label: 255
num_classes: 21
shuffle: True

# backbone
backbone: "dilated_resnet101"
backbone_ckpt_path: "/path/to/resnet101-689c5e77.ckpt"
backbone_ckpt_auto_mapping: False
backbone_features_only: True
backbone_out_indices: [1, 4]
output_stride: 8

# model
model: "deeplabv3plus"
ckpt_pre_trained: True
ckpt_path: "/path/to/pre_trained.ckpt"
amp_level: "O2"
amp_cast_list: None

# scheduler
scheduler: "cosine_decay"
lr: 0.02
min_lr: 0.0000001
decay_epochs: 40000
decay_rate: 0.1
epoch_size: 500
lr_epoch_stair: False

# optimizer
loss_scale_type: "fixed"
drop_overflow_update: False
loss_scale: 2048.0
momentum: 0.9
weight_decay: 0.0001
weight_decay_filter": 'auto'
gradient_accumulation_steps: 1

# callbacks
save_steps: 410
keep_checkpoint_max: 2
ckpt_save_dir: "./ckpt"

# eval
eval_while_train: True
eval_data_lst: "/path/to/voc_val_lst.txt"
data_root: ""
eval_processing_log: False
eval_interval: 2
eval_start_epoch: 50
input_format: "NCHW"
flip: False
scales: [1.0,]
# scales: [0.5, 0.75, 1.0, 1.25, 1.75]