121 lines
6.1 KiB
Python
Executable File
121 lines
6.1 KiB
Python
Executable File
import os, time, copy, torch
|
|
from tqdm import tqdm
|
|
from utils.logger import get_logger
|
|
from utils.loss_function import all_metrics
|
|
|
|
|
|
class Trainer:
|
|
def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader, scaler, args, lr_scheduler=None):
|
|
self.device, self.args = args["basic"]["device"], args["train"]
|
|
self.model, self.loss, self.optimizer, self.lr_scheduler = model.to(self.device), loss, optimizer, lr_scheduler
|
|
self.train_loader, self.val_loader, self.test_loader = train_loader, val_loader or test_loader, test_loader
|
|
self.scaler = scaler
|
|
self.inv = lambda x: torch.cat([s.inverse_transform(x[..., i:i+1]) for i, s in enumerate(self.scaler)], dim=-1) # 对每个维度调用反归一化器后cat
|
|
self._init_paths()
|
|
self._init_logger()
|
|
# ---------- shape magic (replace TSWrapper) ----------
|
|
self.pack = lambda x:(x[..., :-2].permute(0, 2, 1, 3).reshape(-1, x.size(1), x.size(3) - 2), x.shape)
|
|
self.unpack = lambda y, s: (y.reshape(s[0], s[2], s[1], -1).permute(0, 2, 1, 3))
|
|
|
|
# ---------------- init ----------------
|
|
def _init_paths(self):
|
|
d = self.args["log_dir"]
|
|
self.best_path, self.best_test_path = os.path.join(d, "best_model.pth"), os.path.join(d, "best_test_model.pth")
|
|
|
|
def _init_logger(self):
|
|
if not self.args["debug"]: os.makedirs(self.args["log_dir"], exist_ok=True)
|
|
self.logger = get_logger(self.args["log_dir"], name=self.model.__class__.__name__, debug=self.args["debug"])
|
|
|
|
# ---------------- epoch ----------------
|
|
def _run_epoch(self, epoch, loader, mode):
|
|
is_train = mode == "train"
|
|
self.model.train() if is_train else self.model.eval()
|
|
total_loss, start = 0.0, time.time()
|
|
y_pred, y_true = [], []
|
|
|
|
with torch.set_grad_enabled(is_train):
|
|
bar = tqdm(loader, desc=f"{mode} {epoch}", total=len(loader))
|
|
for data, target in bar:
|
|
data, target = data.to(self.device), target.to(self.device)
|
|
label = target[..., :self.args["output_dim"]]
|
|
x, shp = self.pack(data)
|
|
out = self.unpack(self.model(x), shp)
|
|
if os.environ.get("TRY") == "True": print(f"{'[✅]' if out.shape == label.shape else '❌'} "
|
|
f"out: {out.shape}, label: {label.shape} \n"); assert False
|
|
loss = self.loss(out, label)
|
|
d_out, d_lbl = self.inv(out), self.inv(label) # 反归一化
|
|
d_loss = self.loss(d_out, d_lbl)
|
|
total_loss += d_loss.item()
|
|
y_pred.append(d_out.detach().cpu())
|
|
y_true.append(d_lbl.detach().cpu())
|
|
|
|
if is_train and self.optimizer:
|
|
self.optimizer.zero_grad()
|
|
loss.backward()
|
|
if self.args["grad_norm"]: torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args["max_grad_norm"])
|
|
self.optimizer.step()
|
|
bar.set_postfix({"loss": f"{d_loss.item():.4f}"})
|
|
|
|
y_pred, y_true = torch.cat(y_pred), torch.cat(y_true)
|
|
mae, rmse, mape = all_metrics(y_pred, y_true, self.args["mae_thresh"], self.args["mape_thresh"])
|
|
self.logger.info(f"Epoch #{epoch:02d} {mode:<5} MAE:{mae:5.2f} RMSE:{rmse:5.2f} MAPE:{mape:7.4f} Time:{time.time()-start:.2f}s")
|
|
return total_loss / len(loader)
|
|
|
|
# ---------------- train ----------------
|
|
def train(self):
|
|
best, best_test = float("inf"), float("inf")
|
|
best_w, best_test_w = None, None
|
|
patience = 0
|
|
self.logger.info("Training started")
|
|
|
|
for epoch in range(1, self.args["epochs"] + 1):
|
|
losses = {
|
|
"train": self._run_epoch(epoch, self.train_loader, "train"),
|
|
"val": self._run_epoch(epoch, self.val_loader, "val"),
|
|
"test": self._run_epoch(epoch, self.test_loader, "test"),
|
|
}
|
|
|
|
if losses["train"] > 1e6: self.logger.warning("Gradient explosion detected"); break
|
|
if losses["val"] < best:
|
|
best, patience, best_w = losses["val"], 0, copy.deepcopy(self.model.state_dict())
|
|
self.logger.info(f"Best model updated at Epoch {epoch:02d}#")
|
|
else: patience += 1
|
|
if self.args["early_stop"] and patience == self.args["early_stop_patience"]: break
|
|
if losses["test"] < best_test:
|
|
best_test, best_test_w = losses["test"], copy.deepcopy(self.model.state_dict())
|
|
self.logger.info(f"Best test model saved at Epoch {epoch:02d}#")
|
|
|
|
if not self.args["debug"]:
|
|
torch.save(best_w, self.best_path)
|
|
torch.save(best_test_w, self.best_test_path)
|
|
self._final_test(best_w, best_test_w)
|
|
|
|
# ---------------- final test ----------------
|
|
def _final_test(self, best_w, best_test_w):
|
|
for name, w in [("best val", best_w), ("best test", best_test_w)]:
|
|
self.model.load_state_dict(w)
|
|
self.logger.info(f"Testing on {name} model")
|
|
self.evaluate()
|
|
|
|
# ---------------- evaluate ----------------
|
|
def evaluate(self):
|
|
self.model.eval()
|
|
y_pred, y_true = [], []
|
|
|
|
with torch.no_grad():
|
|
for data, target in self.test_loader:
|
|
data, target = data.to(self.device), target.to(self.device)
|
|
label = target[..., :self.args["output_dim"]]
|
|
x, shp = self.pack(data)
|
|
out = self.unpack(self.model(x), shp)
|
|
y_pred.append(out.cpu())
|
|
y_true.append(label.cpu())
|
|
|
|
d_pred, d_true = self.inv(torch.cat(y_pred)), self.inv(torch.cat(y_true)) # 反归一化
|
|
for t in range(d_true.shape[1]):
|
|
mae, rmse, mape = all_metrics(d_pred[:, t], d_true[:, t], self.args["mae_thresh"], self.args["mape_thresh"])
|
|
self.logger.info(f"Horizon {t+1:02d} MAE:{mae:.4f} RMSE:{rmse:.4f} MAPE:{mape:.4f}")
|
|
|
|
avg_mae, avg_rmse, avg_mape = all_metrics(d_pred, d_true, self.args["mae_thresh"], self.args["mape_thresh"])
|
|
self.logger.info(f"AVG MAE:{avg_mae:.4f} AVG RMSE:{avg_rmse:.4f} AVG MAPE:{avg_mape:.4f}")
|