first commit
This commit is contained in:
CownowAn
167
MobileNetV3/configs/tr_meta_surrogate_ofa.py
Normal file
167
MobileNetV3/configs/tr_meta_surrogate_ofa.py
Normal file
@@ -0,0 +1,167 @@
|
||||
import ml_collections
|
||||
import torch
|
||||
from all_path import SCORE_MODEL_CKPT_PATH, SCORE_MODEL_DATA_PATH
|
||||
|
||||
|
||||
def get_config():
|
||||
config = ml_collections.ConfigDict()
|
||||
|
||||
config.search_space = None
|
||||
|
||||
# genel
|
||||
config.resume = False
|
||||
config.folder_name = 'DiffusionNAG'
|
||||
config.task = 'tr_meta_predictor'
|
||||
config.exp_name = None
|
||||
config.model_type = 'meta_predictor'
|
||||
config.scorenet_ckpt_path = SCORE_MODEL_CKPT_PATH
|
||||
config.is_meta = True
|
||||
|
||||
# training
|
||||
config.training = training = ml_collections.ConfigDict()
|
||||
training.sde = 'vesde'
|
||||
training.continuous = True
|
||||
training.reduce_mean = True
|
||||
training.noised = True
|
||||
|
||||
training.batch_size = 128
|
||||
training.eval_batch_size = 512
|
||||
training.n_iters = 20000
|
||||
training.snapshot_freq = 500
|
||||
training.log_freq = 500
|
||||
training.eval_freq = 500
|
||||
## store additional checkpoints for preemption
|
||||
training.snapshot_freq_for_preemption = 1000
|
||||
## produce samples at each snapshot.
|
||||
training.snapshot_sampling = True
|
||||
training.likelihood_weighting = False
|
||||
# training for perturbed data
|
||||
training.t_spot = 1.
|
||||
# training from pretrained score model
|
||||
training.load_pretrained = False
|
||||
training.pretrained_model_path = SCORE_MODEL_CKPT_PATH
|
||||
|
||||
# sampling
|
||||
config.sampling = sampling = ml_collections.ConfigDict()
|
||||
sampling.method = 'pc'
|
||||
sampling.predictor = 'euler_maruyama'
|
||||
sampling.corrector = 'none'
|
||||
# sampling.corrector = 'langevin'
|
||||
sampling.rtol = 1e-5
|
||||
sampling.atol = 1e-5
|
||||
sampling.ode_method = 'dopri5' # 'rk4'
|
||||
sampling.ode_step = 0.01
|
||||
|
||||
sampling.n_steps_each = 1
|
||||
sampling.noise_removal = True
|
||||
sampling.probability_flow = False
|
||||
sampling.snr = 0.16
|
||||
sampling.vis_row = 4
|
||||
sampling.vis_col = 4
|
||||
|
||||
# conditional
|
||||
sampling.classifier_scale = 1.0
|
||||
sampling.regress = True
|
||||
sampling.labels = 'max'
|
||||
sampling.weight_ratio = False
|
||||
sampling.weight_scheduling = False
|
||||
sampling.t_spot = 1.
|
||||
sampling.t_spot_end = 0.
|
||||
sampling.number_chain_steps = 50
|
||||
sampling.check_dataname = 'imagenet1k'
|
||||
|
||||
# evaluation
|
||||
config.eval = evaluate = ml_collections.ConfigDict()
|
||||
evaluate.begin_ckpt = 5
|
||||
evaluate.end_ckpt = 20
|
||||
# evaluate.batch_size = 512
|
||||
evaluate.batch_size = 128
|
||||
evaluate.enable_sampling = True
|
||||
evaluate.num_samples = 1024
|
||||
evaluate.mmd_distance = 'RBF'
|
||||
evaluate.max_subgraph = False
|
||||
evaluate.save_graph = False
|
||||
|
||||
# data
|
||||
config.data = data = ml_collections.ConfigDict()
|
||||
data.centered = True
|
||||
data.dequantization = False
|
||||
|
||||
data.root = SCORE_MODEL_DATA_PATH
|
||||
data.name = 'ofa'
|
||||
data.split_ratio = 0.8
|
||||
data.dataset_idx = 'random'
|
||||
data.max_node = 20
|
||||
data.n_vocab = 9
|
||||
data.START_TYPE = 0
|
||||
data.END_TYPE = 1
|
||||
data.num_graphs = 100000
|
||||
data.num_channels = 1
|
||||
data.except_inout = False # ignore
|
||||
data.triu_adj = True
|
||||
data.connect_prev = False
|
||||
data.tg_dataset = None
|
||||
data.label_list = ['meta-acc']
|
||||
# aug_mask
|
||||
data.aug_mask_algo = 'none' # 'long_range' | 'floyd'
|
||||
# num_train
|
||||
data.num_train = 150
|
||||
|
||||
# model
|
||||
config.model = model = ml_collections.ConfigDict()
|
||||
model.name = 'MetaPredictorCATE'
|
||||
model.ema_rate = 0.9999
|
||||
model.normalization = 'GroupNorm'
|
||||
model.nonlinearity = 'swish'
|
||||
model.nf = 128
|
||||
model.num_gnn_layers = 4
|
||||
model.size_cond = False
|
||||
model.embedding_type = 'positional'
|
||||
model.rw_depth = 16
|
||||
model.graph_layer = 'PosTransLayer'
|
||||
model.edge_th = -1.
|
||||
model.heads = 8
|
||||
model.attn_clamp = False
|
||||
#############################################################################
|
||||
# meta
|
||||
model.input_type = 'DA'
|
||||
model.hs = 512
|
||||
model.nz = 56
|
||||
model.num_sample = 20
|
||||
|
||||
model.num_scales = 1000
|
||||
model.beta_min = 0.1
|
||||
model.beta_max = 5.0
|
||||
model.sigma_min = 0.1
|
||||
model.sigma_max = 5.0
|
||||
model.dropout = 0.1
|
||||
# graph encoder
|
||||
config.model.graph_encoder = graph_encoder = ml_collections.ConfigDict()
|
||||
graph_encoder.n_layers = 2
|
||||
graph_encoder.d_model = 64
|
||||
graph_encoder.n_head = 2
|
||||
graph_encoder.d_ff = 32
|
||||
graph_encoder.dropout = 0.1
|
||||
graph_encoder.n_vocab = 9
|
||||
|
||||
# optimization
|
||||
config.optim = optim = ml_collections.ConfigDict()
|
||||
optim.weight_decay = 0
|
||||
optim.optimizer = 'Adam'
|
||||
optim.lr = 0.001
|
||||
optim.beta1 = 0.9
|
||||
optim.eps = 1e-8
|
||||
optim.warmup = 1000
|
||||
optim.grad_clip = 1.
|
||||
|
||||
config.seed = 42
|
||||
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
# log
|
||||
config.log = log = ml_collections.ConfigDict()
|
||||
log.use_wandb = True
|
||||
log.wandb_project_name = 'DiffusionNAG'
|
||||
log.log_valid_sample_prop = False
|
||||
log.num_graphs_to_visualize = 20
|
||||
|
||||
return config
|
||||
141
MobileNetV3/configs/tr_scorenet_ofa.py
Normal file
141
MobileNetV3/configs/tr_scorenet_ofa.py
Normal file
@@ -0,0 +1,141 @@
|
||||
"""Training PGSN on Community Small Dataset with GraphGDP"""
|
||||
|
||||
import ml_collections
|
||||
import torch
|
||||
|
||||
|
||||
def get_config():
|
||||
config = ml_collections.ConfigDict()
|
||||
|
||||
# general
|
||||
config.resume = False
|
||||
config.resume_ckpt_path = './exp'
|
||||
config.folder_name = 'tr_scorenet'
|
||||
config.task = 'tr_scorenet'
|
||||
config.exp_name = None
|
||||
|
||||
config.model_type = 'sde'
|
||||
|
||||
# training
|
||||
config.training = training = ml_collections.ConfigDict()
|
||||
training.sde = 'vesde'
|
||||
training.continuous = True
|
||||
training.reduce_mean = True
|
||||
|
||||
training.batch_size = 256
|
||||
training.eval_batch_size = 1000
|
||||
training.n_iters = 1000000
|
||||
training.snapshot_freq = 10000
|
||||
training.log_freq = 200
|
||||
training.eval_freq = 10000
|
||||
## store additional checkpoints for preemption
|
||||
training.snapshot_freq_for_preemption = 5000
|
||||
## produce samples at each snapshot.
|
||||
training.snapshot_sampling = True
|
||||
training.likelihood_weighting = False
|
||||
|
||||
# sampling
|
||||
config.sampling = sampling = ml_collections.ConfigDict()
|
||||
sampling.method = 'pc'
|
||||
sampling.predictor = 'euler_maruyama'
|
||||
sampling.corrector = 'none'
|
||||
sampling.rtol = 1e-5
|
||||
sampling.atol = 1e-5
|
||||
sampling.ode_method = 'dopri5' # 'rk4'
|
||||
sampling.ode_step = 0.01
|
||||
|
||||
sampling.n_steps_each = 1
|
||||
sampling.noise_removal = True
|
||||
sampling.probability_flow = False
|
||||
sampling.snr = 0.16
|
||||
sampling.vis_row = 4
|
||||
sampling.vis_col = 4
|
||||
sampling.alpha = 0.5
|
||||
sampling.qtype = 'threshold'
|
||||
|
||||
# evaluation
|
||||
config.eval = evaluate = ml_collections.ConfigDict()
|
||||
evaluate.begin_ckpt = 5
|
||||
evaluate.end_ckpt = 20
|
||||
evaluate.batch_size = 1024
|
||||
evaluate.enable_sampling = True
|
||||
evaluate.num_samples = 1024
|
||||
evaluate.mmd_distance = 'RBF'
|
||||
evaluate.max_subgraph = False
|
||||
evaluate.save_graph = False
|
||||
|
||||
# data
|
||||
config.data = data = ml_collections.ConfigDict()
|
||||
data.centered = True
|
||||
data.dequantization = False
|
||||
|
||||
data.root = './data/ofa/data_score_model/ofa_database_500000.pt'
|
||||
data.name = 'ofa'
|
||||
data.split_ratio = 0.9
|
||||
data.dataset_idx = 'random'
|
||||
data.max_node = 20
|
||||
data.n_vocab = 9 # 10 #
|
||||
data.START_TYPE = 0
|
||||
data.END_TYPE = 1
|
||||
data.num_graphs = 100000
|
||||
data.num_channels = 1
|
||||
data.except_inout = False
|
||||
data.triu_adj = True
|
||||
data.connect_prev = False
|
||||
data.label_list = None
|
||||
data.tg_dataset = None
|
||||
data.node_rule_type = 2
|
||||
# aug_mask
|
||||
data.aug_mask_algo = 'none'
|
||||
|
||||
# model
|
||||
config.model = model = ml_collections.ConfigDict()
|
||||
model.name = 'CATE'
|
||||
model.ema_rate = 0.9999
|
||||
model.normalization = 'GroupNorm'
|
||||
model.nonlinearity = 'swish'
|
||||
model.nf = 128
|
||||
model.num_gnn_layers = 4
|
||||
model.size_cond = False
|
||||
model.embedding_type = 'positional'
|
||||
model.rw_depth = 16
|
||||
model.graph_layer = 'PosTransLayer'
|
||||
model.edge_th = -1.
|
||||
model.heads = 8
|
||||
model.attn_clamp = False
|
||||
|
||||
model.num_scales = 1000
|
||||
model.sigma_min = 0.1
|
||||
model.sigma_max = 1.0
|
||||
model.dropout = 0.1
|
||||
model.pos_enc_type = 2
|
||||
# graph encoder
|
||||
config.model.graph_encoder = graph_encoder = ml_collections.ConfigDict()
|
||||
graph_encoder.n_layers = 12
|
||||
graph_encoder.d_model = 64
|
||||
graph_encoder.n_head = 8
|
||||
graph_encoder.d_ff = 128
|
||||
graph_encoder.dropout = 0.1
|
||||
graph_encoder.n_vocab = 9 #10 # 30
|
||||
|
||||
# optimization
|
||||
config.optim = optim = ml_collections.ConfigDict()
|
||||
optim.weight_decay = 0
|
||||
optim.optimizer = 'Adam'
|
||||
optim.lr = 2e-5
|
||||
optim.beta1 = 0.9
|
||||
optim.eps = 1e-8
|
||||
optim.warmup = 1000
|
||||
optim.grad_clip = 1.
|
||||
|
||||
config.seed = 42
|
||||
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
# log
|
||||
config.log = log = ml_collections.ConfigDict()
|
||||
log.use_wandb = True
|
||||
log.wandb_project_name = 'DiffusionNAG'
|
||||
log.log_valid_sample_prop = False
|
||||
log.num_graphs_to_visualize = 20
|
||||
|
||||
return config
|
||||
Reference in New Issue
Block a user