mods

examples/monai-2D-mednist/client/train.py

@@ -59,7 +59,7 @@ def __getitem__(self, index):
     train_ds = MedNISTDataset(train_x, train_y, train_transforms)
     train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=num_workers )
 
-    return train_ds, train_loader 
+    return train_loader
 
 
 def train(in_model_path, out_model_path, data_path=None, client_settings_path=None):
@@ -94,25 +94,20 @@ def train(in_model_path, out_model_path, data_path=None, client_settings_path=No
     sample_size = client_settings['sample_size']
     lr = client_settings['lr']
 
-    #val_interval = 1
     num_class = len(get_classes(data_path))
 
     # Load data
-    x_train, y_train  = load_data(data_path, sample_size)
-    train_ds, train_loader = pre_training_settings(num_class, batch_size, x_train, y_train, num_workers)
-
+    x_train, y_train = load_data(data_path, sample_size)
+    train_loader = pre_training_settings(num_class, batch_size, x_train, y_train, num_workers)
 
     # Load parmeters and initialize model
     model = load_parameters(in_model_path)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
     optimizer = torch.optim.Adam(model.parameters(), 1e-5)
     loss_function = torch.nn.CrossEntropyLoss()
+
     # Train
-
-    best_metric = -1
-    best_metric_epoch = -1
     epoch_loss_values = []
-    metric_values = []
     # writer = SummaryWriter()
 
     for epoch in range(max_epochs):
@@ -130,9 +125,8 @@ def train(in_model_path, out_model_path, data_path=None, client_settings_path=No
             loss.backward()
             optimizer.step()
             epoch_loss += loss.item()
-            print(f"{step}/{len(train_ds) // train_loader.batch_size}, " f"train_loss: {loss.item():.4f}")
-            epoch_len = len(train_ds) // train_loader.batch_size
-            # writer.add_scalar("train_loss", loss.item(), epoch_len * epoch + step)
+            print(f"{step}/{len(sample_size) // train_loader.batch_size}, " f"train_loss: {loss.item():.4f}")
+
         epoch_loss /= step
         epoch_loss_values.append(epoch_loss)
         print(f"epoch {epoch + 1} average loss: {epoch_loss:.4f}")

examples/monai-2D-mednist/client/validate.py

@@ -36,18 +36,7 @@
 sys.path.append(os.path.abspath(dir_path))
 
 
-def pre_validation_settings(num_class, batch_size, train_x, train_y, val_x, val_y, num_workers=2):
-
-    train_transforms = Compose(
-        [
-            LoadImage(image_only=True),
-            EnsureChannelFirst(),
-            ScaleIntensity(),
-            RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),
-            RandFlip(spatial_axis=0, prob=0.5),
-            RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
-        ]
-    )
+def pre_validation_settings(batch_size, train_x, train_y, val_x, val_y, num_workers=2):
 
     val_transforms = Compose([LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity()])
 
@@ -63,13 +52,11 @@ def __len__(self):
         def __getitem__(self, index):
             return self.transforms(self.image_files[index]), self.labels[index]
 
-    train_ds = MedNISTDataset(train_x, train_y, val_transforms)
-    train_loader = DataLoader(train_ds, batch_size=batch_size, num_workers=num_workers)
 
     val_ds = MedNISTDataset(val_x, val_y, val_transforms)
     val_loader = DataLoader(val_ds, batch_size=batch_size, num_workers=num_workers)
 
-    return train_ds, train_loader, val_ds, val_loader
+    return val_loader
 
 
 
@@ -103,9 +90,8 @@ def validate(in_model_path, out_json_path, data_path=None, client_settings_path=
     # Load data
     x_train, y_train = load_data(data_path, sample_size)
     x_val, y_val = load_data(data_path, sample_size, is_train=False)
-
-    num_class = len(get_classes(data_path))
-    train_ds, train_loader, val_ds, val_loader = pre_validation_settings(num_class, batch_size,  x_train, y_train, x_val, y_val, num_workers)
+
+    val_loader = pre_validation_settings(batch_size,  x_train, y_train, x_val, y_val, num_workers)
 
     # Load model
     model = load_parameters(in_model_path)