update tutorial with new features

leopard-ai · May 29, 2023 · 651ccdf · 651ccdf
1 parent 69c8902
commit 651ccdf
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Showing 5 changed files with 9 additions and 23 deletions.
diff --git a/tutorial/1_quick_start.py b/tutorial/1_quick_start.py
@@ -99,7 +99,6 @@ def training_step(self, batch):
     optimizer=reweight_optimizer,
     train_data_loader=reweight_dataloader,
     config=reweight_config,
-    device=device,
 )
 
 ####################
@@ -138,7 +137,6 @@ def training_step(self, batch):
     scheduler=classifier_scheduler,
     train_data_loader=classifier_dataloader,
     config=classifier_config,
-    device=device,
 )
 
 

diff --git a/tutorial/2_validation.py b/tutorial/2_validation.py
@@ -71,8 +71,6 @@ def build_dataset(reweight_size=1000, imbalanced_factor=100):
 valid_dataset = MNIST(root="./data", train=False, transform=transform)
 valid_dataloader = DataLoader(valid_dataset, batch_size=100, pin_memory=True)
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 
 ####################
 ##### Reweight #####
@@ -102,7 +100,6 @@ def training_step(self, batch):
     optimizer=reweight_optimizer,
     train_data_loader=reweight_dataloader,
     config=reweight_config,
-    device=device,
 )
 
 ####################
@@ -139,7 +136,6 @@ def training_step(self, batch):
     scheduler=classifier_scheduler,
     train_data_loader=classifier_dataloader,
     config=classifier_config,
-    device=device,
 )
 
 
@@ -151,15 +147,15 @@ def validation(self):
         if not hasattr(self, "best_acc"):
             self.best_acc = -1
         for x, target in valid_dataloader:
-            x, target = x.to(device), target.to(device)
+            x, target = x.to(self.device), target.to(self.device)
             out = self.classifier(x)
             correct += (out.argmax(dim=1) == target).sum().item()
             total += x.size(0)
         acc = correct / total * 100
         if self.best_acc < acc:
             self.best_acc = acc
 
-        print("acc:", acc, "best_acc:", self.best_acc)
+        return {"acc": acc, "best_acc": self.best_acc}
 
 
 engine_config = EngineConfig(train_iters=3000, valid_step=100)

diff --git a/tutorial/3_logging.py b/tutorial/3_logging.py
@@ -71,8 +71,6 @@ def build_dataset(reweight_size=1000, imbalanced_factor=100):
 valid_dataset = MNIST(root="./data", train=False, transform=transform)
 valid_dataloader = DataLoader(valid_dataset, batch_size=100, pin_memory=True)
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 
 ####################
 ##### Reweight #####
@@ -103,7 +101,6 @@ def training_step(self, batch):
     optimizer=reweight_optimizer,
     train_data_loader=reweight_dataloader,
     config=reweight_config,
-    device=device,
 )
 
 ####################
@@ -140,7 +137,6 @@ def training_step(self, batch):
     scheduler=classifier_scheduler,
     train_data_loader=classifier_dataloader,
     config=classifier_config,
-    device=device,
 )
 
 
@@ -152,7 +148,7 @@ def validation(self):
         if not hasattr(self, "best_acc"):
             self.best_acc = -1
         for x, target in valid_dataloader:
-            x, target = x.to(device), target.to(device)
+            x, target = x.to(self.device), target.to(self.device)
             out = self.classifier(x)
             correct += (out.argmax(dim=1) == target).sum().item()
             total += x.size(0)

diff --git a/tutorial/4_memory_optimization.py b/tutorial/4_memory_optimization.py
@@ -18,7 +18,7 @@
 from betty.configs import Config, EngineConfig
 
 
-fp16 = True
+precision = "fp16"
 distributed = True
 
 
@@ -112,14 +112,13 @@ def training_step(self, batch):
         return {"loss": loss, "acc": acc}
 
 
-reweight_config = Config(log_step=100, fp16=fp16)
+reweight_config = Config(log_step=100, precision=precision)
 reweight = Reweight(
     name="reweight",
     module=reweight_module,
     optimizer=reweight_optimizer,
     train_data_loader=reweight_dataloader,
     config=reweight_config,
-    device=device,
 )
 
 ####################
@@ -223,15 +222,14 @@ def training_step(self, batch):
         return torch.mean(weight * loss_reshape)
 
 
-classifier_config = Config(type="darts", unroll_steps=1, fp16=fp16)
+classifier_config = Config(type="darts", unroll_steps=1, precision=precision)
 classifier = Classifier(
     name="classifier",
     module=classifier_module,
     optimizer=classifier_optimizer,
     scheduler=classifier_scheduler,
     train_data_loader=classifier_dataloader,
     config=classifier_config,
-    device=device,
 )
 
 

diff --git a/tutorial/5_distributed_training.py b/tutorial/5_distributed_training.py
@@ -89,8 +89,6 @@ def build_dataset(reweight_size=1000, imbalanced_factor=100):
 valid_dataset = CIFAR10(root="./data", train=False, transform=valid_transform)
 valid_dataloader = DataLoader(valid_dataset, batch_size=50, pin_memory=True)
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
 
 ####################
 ##### Reweight #####
@@ -114,7 +112,7 @@ def training_step(self, batch):
         return {"loss": loss, "acc": acc}
 
 
-reweight_config = Config(log_step=100, fp16=True)
+reweight_config = Config(log_step=100, precision="fp16")
 reweight = Reweight(
     name="reweight",
     module=reweight_module,
@@ -224,7 +222,7 @@ def training_step(self, batch):
         return torch.mean(weight * loss_reshape)
 
 
-classifier_config = Config(type="darts", unroll_steps=1, fp16=True)
+classifier_config = Config(type="darts", unroll_steps=1, precision="fp16")
 classifier = Classifier(
     name="classifier",
     module=classifier_module,
@@ -243,7 +241,7 @@ def validation(self):
         if not hasattr(self, "best_acc"):
             self.best_acc = -1
         for x, target in valid_dataloader:
-            x, target = x.to(device), target.to(device)
+            x, target = x.to(self.device), target.to(self.device)
             out = self.classifier(x)
             correct += (out.argmax(dim=1) == target).sum().item()
             total += x.size(0)