File size: 3,013 Bytes
0161e74 | 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 | from dataclasses import dataclass
import os
@dataclass
class CascadedFlowConfig:
# === Base (same as scDFM FlowConfig) ===
model_type: str = "cascaded"
batch_size: int = 48
ntoken: int = 512
d_model: int = 128
nhead: int = 8
nlayers: int = 4
lr: float = 5e-5
steps: int = 200000
eta_min: float = 1e-6
devices: str = "1"
test_only: bool = False
data_name: str = "norman"
perturbation_function: str = "crisper"
noise_type: str = "Gaussian"
poisson_alpha: float = 0.8
poisson_target_sum: int = -1
print_every: int = 5000
mode: str = "predict_y"
result_path: str = "./result_online"
fusion_method: str = "differential_perceiver"
infer_top_gene: int = 1000
n_top_genes: int = 5000
checkpoint_path: str = ""
gamma: float = 0.5
split_method: str = "additive"
use_mmd_loss: bool = True
fold: int = 1
use_negative_edge: bool = True
topk: int = 30
# === Cascaded / Latent specific ===
scgpt_dim: int = 512
bottleneck_dim: int = 128
latent_weight: float = 1.0
choose_latent_p: float = 0.4
target_std: float = 1.0
dh_depth: int = 2
warmup_batches: int = 200 # batches to collect running stats
# === Cascaded noise (LatentForcing dino_first_cascaded_noised) ===
noise_beta: float = 0.25 # when training expr flow, t_latent ~ U[1-beta, 1] instead of 1.0
# === EMA ===
ema_decay: float = 0.9999
# === Logit-normal time-step sampling ===
t_sample_mode: str = "logit_normal" # "uniform" or "logit_normal"
t_expr_mean: float = 0.0 # expression flow logit-normal mu
t_expr_std: float = 1.0 # expression flow logit-normal sigma
t_latent_mean: float = 0.0 # latent flow logit-normal mu
t_latent_std: float = 1.0 # latent flow logit-normal sigma
# === LR warmup ===
warmup_steps: int = 2000
# === scGPT paths ===
scgpt_model_dir: str = "transfer/data/scGPT_pretrained"
scgpt_max_seq_len: int = 1200
scgpt_cache_path: str = "" # Pre-extracted HDF5 path. Empty = online extraction (default)
# === Inference ===
latent_steps: int = 20
expr_steps: int = 20
ode_method: str = "rk4" # "euler" or "rk4"
def __post_init__(self):
if self.data_name == "norman_umi_go_filtered":
self.n_top_genes = 5054
if self.data_name == "norman":
self.n_top_genes = 5000
def make_path(self):
scgpt_mode = "cached" if self.scgpt_cache_path else "online"
t_mode = "ln" if self.t_sample_mode == "logit_normal" else "uni"
exp_name = (
f"ccfm-{self.data_name}-f{self.fold}"
f"-topk{self.topk}-neg{self.use_negative_edge}"
f"-d{self.d_model}-lr{self.lr}"
f"-lw{self.latent_weight}-lp{self.choose_latent_p}"
f"-ema{self.ema_decay}-{t_mode}-wu{self.warmup_steps}"
f"-{self.ode_method}-{scgpt_mode}"
)
return os.path.join(self.result_path, exp_name)
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