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| import logging
import os
from typing import Any, Dict, Optional, Union, Tuple
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.utils.checkpoint import checkpoint # New
from tqdm import tqdm
import numpy as np # New
import csv # New
from sssd.core.model_specs import MASK_FN
from sssd.training.utils import training_loss
from sssd.data.utils import get_dataloader
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, std_normal # New
from autoFRK import AutoFRK, to_tensor, garbage_cleaner # New
from autoFRK.utils.helper import cbrt # New
LOGGER = setup_logger()
class DiffusionTrainer:
"""
Train Diffusion Models
Args:
dataloader (DataLoader): The training dataloader.
diffusion_hyperparams (Dict[str, Any]): Hyperparameters for the diffusion process.
net (nn.Module): The neural network model to be trained.
device (torch.device): The device to be used for training.
output_directory (str): Directory to save model checkpoints.
ckpt_iter (Optional[int, str]): The checkpoint iteration to be loaded; 'max' selects the maximum iteration.
n_iters (int): Number of iterations to train.
iters_per_ckpt (int): Number of iterations to save checkpoint.
iters_per_logging (int): Number of iterations to save training log and compute validation loss.
learning_rate (float): Learning rate for training.
only_generate_missing (int): Option to generate missing portions of the signal only.
masking (str): Type of masking strategy: 'mnr' for Missing Not at Random, 'bm' for Blackout Missing, 'rm' for Random Missing.
missing_k (int): K missing time steps for each feature across the sample length.
batch_size (int): Size of each training batch.
logger (Optional[logging.Logger]): Logger object for logging, defaults to None.
"""
def __init__(
self,
data_path: str,
diffusion_hyperparams: Dict[str, Any],
net: nn.Module,
device: Optional[Union[torch.device, str]],
output_directory: str,
ckpt_iter: Union[str, int],
n_iters: int,
iters_per_ckpt: int,
iters_per_logging: int,
learning_rate: float,
only_generate_missing: int,
masking: str,
missing_k: int,
batch_size: int,
enable_spatial_prediction: bool, # New
autoFRK_period: int, # New
location_path: str, # New
AFRK_method: str, # New
AFRK_tps_method: str, # New
logger: Optional[logging.Logger] = None,
) -> None:
loader, ts_mean, ts_std = get_dataloader(
path=data_path,
batch_size=batch_size,
device=device,
)
self.dataloader = loader
self.ts_mean = ts_mean
self.ts_std = ts_std
self.diffusion_hyperparams = diffusion_hyperparams
self.net = nn.DataParallel(net).to(device)
self.device = device
self.output_directory = output_directory
self.ckpt_iter = ckpt_iter
self.n_iters = n_iters
self.iters_per_ckpt = iters_per_ckpt
self.iters_per_logging = iters_per_logging
self.learning_rate = learning_rate
self.only_generate_missing = only_generate_missing
self.masking = masking
self.missing_k = missing_k
self.writer = SummaryWriter(f"{output_directory}/log")
self.batch_size = batch_size
self.optimizer = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
self.enable_spatial_prediction = enable_spatial_prediction # New
self.autoFRK_period = autoFRK_period # New
self.location_path = location_path # New
self.real_data = self.dataloader.dataset.tensors[0].to(self.device) # * self.ts_std + self.ts_mean # New
self.real_data_shape = self.real_data.shape # New
self.logger = logger or LOGGER
self.AFRK_mrts = None # New
self.AFRK_method = AFRK_method # New
self.AFRK_tps_method = AFRK_tps_method # New
self.loc = to_tensor(np.load(self.location_path), dtype=self.real_data.dtype, device=device)
frk = AutoFRK(
logger_level=30,
dtype=self.real_data.dtype,
device=self.real_data.device,
)
# autoFRK
def frk_step(data_slice, mrts):
_ = frk.forward(
data=data_slice,
loc=self.loc,
G=mrts,
method=self.AFRK_method,
tps_method=self.AFRK_tps_method,
requires_grad=True
)
pred = frk.predict()['pred.value']
return pred.T, frk.obj['G']
#self.frk_step = torch.compile(frk_step)
self.frk_step = frk_step
if self.masking not in MASK_FN:
raise KeyError(f"Please enter a correct masking, but got {self.masking}")
def _load_checkpoint(self) -> None:
if self.ckpt_iter == "max":
self.ckpt_iter = find_max_epoch(self.output_directory)
if self.ckpt_iter >= 0:
try:
model_path = os.path.join(
self.output_directory, f"{self.ckpt_iter}.pkl"
)
checkpoint = torch.load(model_path, map_location="cpu")
self.net.load_state_dict(checkpoint["model_state_dict"])
if "optimizer_state_dict" in checkpoint:
self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
self.logger.info(
f"Successfully loaded model at iteration {self.ckpt_iter}"
)
except Exception as e:
self.ckpt_iter = -1
self.logger.error(f"No valid checkpoint model found. Error: {e}")
else:
self.ckpt_iter = -1
self.logger.info(
"No valid checkpoint model found, start training from initialization."
)
def _save_model(self, n_iter: int) -> None:
if n_iter > 0 and n_iter % self.iters_per_ckpt == 0:
torch.save(
{
"model_state_dict": self.net.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
},
os.path.join(self.output_directory, f"{n_iter}.pkl"),
)
def _update_mask(self, batch: torch.Tensor) -> torch.Tensor:
transposed_mask = MASK_FN[self.masking](batch[0], self.missing_k)
return (
transposed_mask.permute(1, 0)
.repeat(batch.size()[0], 1, 1)
.to(self.device, dtype=torch.float32)
)
def _sampling_differentiable(
self,
net: torch.nn.Module,
size: Tuple[int, int, int],
diffusion_hyperparams: Dict[str, torch.Tensor],
cond: torch.Tensor,
mask: torch.Tensor,
only_generate_missing: int = 0,
device: Union[torch.device, str] = "cpu",
sampling_mode: str = "ddpm", # ✅ "ddpm" 或 "ddim"
step_skip: Union[int, None] = None # ✅ 若為 None，則自動偵測
) -> torch.Tensor:
_dh = diffusion_hyperparams
T, Alpha, Alpha_bar, Sigma = _dh["T"], _dh["Alpha"], _dh["Alpha_bar"], _dh["Sigma"]
x = torch.randn(size, device=device)
mask = mask.to(device)
cond = cond.to(device)
# ✅ 自動偵測最適 step_skip
if step_skip is None:
if T <= 200:
step_skip = 1 # 小模型：全步精細推論
elif T <= 1000:
step_skip = max(1, T // 200) # 一般模型：約 200 步內完成
else:
step_skip = max(1, T // 400) # 大型模型：控制在 400 步以內
#LOGGER.info(f"T={T}, using step_skip={step_skip}")
step_skip = max(1, step_skip)
# ✅ 建立跳步時間表
timesteps = list(range(T - 1, -1, -step_skip))
if timesteps[-1] != 0:
timesteps.append(0)
noise_list = []
if sampling_mode == "ddpm":
for _ in timesteps:
# pre-generate noise with same shape as x; deterministic for forward+recompute
noise_list.append(torch.randn(size, device=device))
else:
# keep placeholder list (not used for ddim)
noise_list = [None] * len(timesteps)
for idx, t in enumerate(timesteps):
noise_t = noise_list[idx]
def net_step(x_inner, noise=noise_t, tt=t):
if only_generate_missing == 1:
x_inner = x_inner * (1 - mask).float() + cond * mask.float()
diffusion_steps = (tt * torch.ones((size[0], 1), device=device))
epsilon_theta = net((x_inner, cond, mask, diffusion_steps))
# ✅ 根據 sampling_mode 決定取樣公式
if sampling_mode == "ddpm":
# DDPM：隨機性版本
x_next = (x_inner - (1 - Alpha[tt]) / torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha[tt])
if tt > 0:
x_next = x_next + Sigma[tt] * noise
elif sampling_mode == "ddim":
# DDIM：確定性版本，支援跳步
if tt == 0:
x_next = torch.sqrt(Alpha_bar[tt]) * (
(x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta)
/ torch.sqrt(Alpha_bar[tt])
)
else:
Alpha_bar_prev = Alpha_bar[max(tt - step_skip, 0)]
x0_pred = (x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha_bar[tt])
x_next = torch.sqrt(Alpha_bar_prev) * x0_pred + torch.sqrt(1 - Alpha_bar_prev) * epsilon_theta
else:
raise ValueError(f"Unknown sampling_mode: {sampling_mode}")
return x_next
x = checkpoint(net_step, x, use_reentrant=False)
#x = net_step(x)
return x
def _sssd_prediction_step(self) -> torch.Tensor:
self.logger.info(f"Start SSSD prediction step")
all_batches = []
for (batch,) in tqdm(self.dataloader, desc=f"{self.n_iter}-th predicting TS"):
mask = self._update_mask(batch)
batch = batch.permute(0, 2, 1)
batch_generated = self._sampling_differentiable(
net=self.net,
size=batch.shape,
diffusion_hyperparams=self.diffusion_hyperparams,
cond=batch,
mask=mask,
only_generate_missing=self.only_generate_missing,
device=self.device,
sampling_mode=getattr(self, "sampling_mode", "ddim"),
step_skip=getattr(self, "step_skip", 10)
)
all_batches.append(batch_generated)
sssd_prediction = torch.cat(all_batches, dim=0).permute(1, 2, 0)
return sssd_prediction
def _autoFRK_step(self,
sssd_prediction,
) -> torch.Tensor:
# autoFRK
self.logger.info(f"Start autoFRK inference step")
dtype = sssd_prediction.dtype
device = sssd_prediction.device
V, T, N = sssd_prediction.shape
autoFRK_result = torch.empty_like(sssd_prediction, dtype=dtype, device=device)
frk_step = self.frk_step
# mrts
if self.AFRK_mrts is None:
data_slice = sssd_prediction[0, :, :].T
_, mrts = frk_step(data_slice, None)
self.AFRK_mrts = mrts
else:
mrts = self.AFRK_mrts
# Flatten 迴圈
for idx in tqdm(range(V), desc=f"{self.n_iter}-th predicting autoFRK"):
data_slice = sssd_prediction[idx, :, :].T
try:
pred, _ = frk_step(data_slice, mrts)
except Exception as e:
self.logger.warning(f"Variable {idx} numerical issue: {e}; using fallback clone")
pred = data_slice.clone()
autoFRK_result[idx, :, :] = pred
nan_idx = torch.isnan(autoFRK_result)
if nan_idx.any():
self.logger.info(f"Replacing total {nan_idx.sum().item()} NaNs in autoFRK_result with original sssd_prediction values")
autoFRK_result = torch.where(nan_idx, sssd_prediction, autoFRK_result)
autoFRK_result = autoFRK_result.permute(2, 1, 0)
# return
if autoFRK_result.shape != self.real_data_shape:
error_msg = f"Shape mismatch: autoFRK_result {autoFRK_result.shape} != real_data {self.real_data_shape}"
self.logger.error(error_msg)
raise ValueError(error_msg)
return autoFRK_result
def _train_per_epoch(self) -> torch.Tensor:
loss_function=nn.MSELoss()
#diffusion_loss = torch.tensor(0.0, device=self.device, dtype=torch.float32)
#n_batches = 0
# SSSD training
for batch_idx, (batch,) in enumerate(tqdm(self.dataloader, desc=f"{self.n_iter}-th training TS")):
batch = batch.to(self.device)
# New
mask = self._update_mask(batch)
loss_mask = ~mask.bool()
self.optimizer.zero_grad()
if loss_mask.sum() == 0:
self.logger.warning(f"Batch {batch_idx} has no valid elements for loss")
continue
batch = batch.permute(0, 2, 1)
assert batch.size() == mask.size() == loss_mask.size()
loss = training_loss(
model=self.net,
loss_function=loss_function,
training_data=(batch, batch, mask, loss_mask),
diffusion_parameters=self.diffusion_hyperparams,
generate_only_missing=self.only_generate_missing,
device=self.device,
)
if not (self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0):
loss.backward()
self.optimizer.step()
continue
loss.backward(retain_graph=True)
self.optimizer.step()
#diffusion_loss = diffusion_loss + loss
#n_batches += 1
if self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0:
self.logger.info(f"Iteration {self.n_iter}: Start Spatial Prediction step")
sssd_prediction = self._sssd_prediction_step()
autoFRK_result = self._autoFRK_step(sssd_prediction=sssd_prediction)
# compute loss
self.optimizer.zero_grad()
spatial_loss = loss_function(
autoFRK_result,
self.real_data
)
#loss = spatial_loss + diffusion_loss / n_batches
loss = spatial_loss
# update model
loss.backward()
self.optimizer.step()
self.logger.info(f"Iteration {self.n_iter}: loss: {loss.item()}")
return loss
def train(self) -> None:
self._load_checkpoint()
n_iter_start = (
self.ckpt_iter + 2 if self.ckpt_iter == -1 else self.ckpt_iter + 1
)
self.logger.info(f"Start the {n_iter_start} iteration")
for n_iter in range(n_iter_start, self.n_iters + 1):
self.n_iter = n_iter
loss = self._train_per_epoch()
self.writer.add_scalar("Train/Loss", loss.item(), n_iter)
if n_iter % self.iters_per_logging == 0:
self.logger.info(f"Iteration: {n_iter} \tLoss: { loss.item()}")
self._save_model(n_iter)
|
