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显示反归一化loss

This commit is contained in:
czzhangheng 2025-11-18 10:46:08 +08:00
parent 8b7e13df30
commit fe3fc186be
3 changed files with 8 additions and 5 deletions
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2
config/REPST/PEMS-BAY.yaml
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@ -1,7 +1,7 @@
basic: basic:
dataset: "PEMS-BAY" dataset: "PEMS-BAY"
mode : "train" mode : "train"
device : "cuda:1" device : "cuda:0"
model: "REPST" model: "REPST"
seed: 2023 seed: 2023

2
run.py
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@ -46,7 +46,7 @@ def main():
model.load_state_dict( model.load_state_dict(
torch.load( torch.load(
f"./pre-trained/{args['basic']['model']}/{args['basic']['dataset']}.pth", f"./pre-trained/{args['basic']['model']}/{args['basic']['dataset']}.pth",
map_location=args["device"], map_location=args["basic"]["device"],
weights_only=True, weights_only=True,
) )
) )

9
trainer/Trainer.py
View File

@ -180,8 +180,8 @@ class Trainer:
loss = self.loss(output, label) loss = self.loss(output, label)
# 反归一化 # 反归一化
self.scaler.inverse_transform(output) d_output = self.scaler.inverse_transform(output)
self.scaler.inverse_transform(label) d_label = self.scaler.inverse_transform(label)
# 反向传播和优化（仅在训练模式） # 反向传播和优化（仅在训练模式）
if optimizer_step and self.optimizer is not None: if optimizer_step and self.optimizer is not None:
@ -195,6 +195,9 @@ class Trainer:
) )
self.optimizer.step() self.optimizer.step()
# 反归一化的loss
d_loss = self.loss(d_output, d_label)
# 记录步骤时间和内存使用 # 记录步骤时间和内存使用
step_time = time.time() - start_time step_time = time.time() - start_time
self.stats.record_step_time(step_time, mode) self.stats.record_step_time(step_time, mode)
@ -205,7 +208,7 @@ class Trainer:
y_true.append(label.detach().cpu()) y_true.append(label.detach().cpu())
# 更新进度条 # 更新进度条
progress_bar.set_postfix(loss=loss.item()) progress_bar.set_postfix(loss=d_loss.item())
# 合并所有批次的预测结果 # 合并所有批次的预测结果
y_pred = torch.cat(y_pred, dim=0) y_pred = torch.cat(y_pred, dim=0)