File size: 1,682 Bytes
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flag_return_losses: True
pq_len: &pq_len 29
pv_len: &pv_len 9
slack_len: &slack_len 1
mask_num: &mask_num 0
batch_size: &batch_size 256
data:
meta:
node: ['PQ', 'PV', 'Slack']
edge:
- ['PQ', 'default', 'PQ']
- ['PQ', 'default', 'PV']
- ['PQ', 'default', 'Slack']
- ['PV', 'default', 'PQ']
- ['PV', 'default', 'PV']
- ['PV', 'default', 'Slack']
- ['Slack', 'default', 'PQ']
- ['Slack', 'default', 'PV']
train:
dataset_type: PowerFlowDataset
data_root: /
split_txt: ./datasets/power/case39_data/10w_case39_n_n_1.json
pq_len: *pq_len
pv_len: *pv_len
slack_len: *slack_len
mask_num: *mask_num
val:
dataset_type: PowerFlowDataset
data_root: /
split_txt: ./datasets/power/case39_data/2w_case39_n_2.json
pq_len: *pq_len
pv_len: *pv_len
slack_len: *slack_len
mask_num: *mask_num
batch_size: *batch_size
batch_size_test: *batch_size
num_workers: 4
train:
logs_freq: 10
epochs: 100
optimizer_type: "Adam"
learning_rate: 0.001
momentum: 0.9
weight_decay: 0.0
model:
type: senseflow
hidden_channels: 128
num_block: 4
layers_per_graph: 2
heads_ca: 8
batch_size: *batch_size
flag_use_edge_feat: False
with_norm: True
num_loops_train: 1
num_loops_test: -1
scaling_factor_vm: 0.01
scaling_factor_va: 0.01
loss_type: l1
flag_weighted_loss: True
loss_weight_equ: 0.1
loss_weight_vm: 10.0
loss_weight_va: 1.0
matrix: vm_va
resume_ckpt_path: ""
flag_use_ema: True
ema_warmup_epoch: 10
ema_decay_param: 0.99
scheduler:
type: Cosine
eta_min: 1e-5
loss:
type: bi_deltapq_loss
filt_type: True
aggr: abs
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