Init ptb

2024-09-13 16:58:34 +08:00
commit b06a9ecee0
103 changed files with 5440 additions and 0 deletions
--- a/PytorchBoot/factory/optimizer_factory.py
+++ b/PytorchBoot/factory/optimizer_factory.py
@@ -0,0 +1,67 @@
+import torch.optim as optim
+
+class OptimizerFactory:
+    @staticmethod
+    def create(config: dict, params) -> optim.Optimizer:
+        optim_type = config["type"]
+        lr = config.get("lr", 1e-3)
+        
+        if optim_type == "SGD":
+            return optim.SGD(
+                params,
+                lr=lr,
+                momentum=config.get("momentum", 0.9),
+                weight_decay=config.get("weight_decay", 1e-4),
+            )
+        elif optim_type == "Adam":
+            return optim.Adam(
+                params,
+                lr=lr,
+                betas=config.get("betas", (0.9, 0.999)),
+                eps=config.get("eps", 1e-8),
+            )
+        elif optim_type == "AdamW":
+            return optim.AdamW(
+                params,
+                lr=lr,
+                betas=config.get("betas", (0.9, 0.999)),
+                eps=config.get("eps", 1e-8),
+                weight_decay=config.get("weight_decay", 1e-2),
+            )
+        elif optim_type == "RMSprop":
+            return optim.RMSprop(
+                params,
+                lr=lr,
+                alpha=config.get("alpha", 0.99),
+                eps=config.get("eps", 1e-8),
+                weight_decay=config.get("weight_decay", 1e-4),
+                momentum=config.get("momentum", 0.9),
+            )
+        elif optim_type == "Adagrad":
+            return optim.Adagrad(
+                params,
+                lr=lr,
+                lr_decay=config.get("lr_decay", 0),
+                weight_decay=config.get("weight_decay", 0),
+            )
+        elif optim_type == "Adamax":
+            return optim.Adamax(
+                params,
+                lr=lr,
+                betas=config.get("betas", (0.9, 0.999)),
+                eps=config.get("eps", 1e-8),
+                weight_decay=config.get("weight_decay", 0),
+            )
+        elif optim_type == "LBFGS":
+            return optim.LBFGS(
+                params,
+                lr=lr,
+                max_iter=config.get("max_iter", 20),
+                max_eval=config.get("max_eval", None),
+                tolerance_grad=config.get("tolerance_grad", 1e-7),
+                tolerance_change=config.get("tolerance_change", 1e-9),
+                history_size=config.get("history_size", 100),
+                line_search_fn=config.get("line_search_fn", None),
+            )
+        else:
+            raise NotImplementedError("Unknown optimizer: {}".format(optim_type))