segmentation_updated

UniTrain/dataset/DCGAN.py

@@ -3,7 +3,6 @@
 from PIL import Image
 import torch
 from torch.utils.data import Dataset
-import wandb
 
 class DCGANdataset:
     def __init__(self, data_dir, transform=None):
@@ -35,19 +34,4 @@ def __getitem__(self, idx):
         if self.transform is not None:
             image = self.transform(image)
 
-        return image, target
-
-# Initialize wandb
-wandb.init(project="your_project_name", entity="your_entity_name")
-
-# Your training code here
-# Log the model architecture
-wandb.watch(model)
-
-for epoch in range(num_epochs):
-    # Your training code here
-    wandb.log({"model_weights": model.state_dict()})
-    # Log images or other metrics as needed
-
-# Finish the wandb run
-wandb.finish()
+        return image, target

UniTrain/dataset/segmentation.py

@@ -4,13 +4,26 @@
 from torch.utils.data import Dataset
 import cv2
 import torchvision.transforms as transforms
+import torchvision.models as models
 
 class SegmentationDataset(Dataset):
-    def __init__(self, image_paths: list, mask_paths: list, transform=None):
+    def __init__(self, image_paths: list, mask_paths: list, transform=None, base_model_weights_path=None):
         self.image_paths = image_paths
         self.mask_paths = mask_paths
         self.transform = transform
 
+        # Load base model for transfer learning
+        self.base_model = self.load_base_model(base_model_weights_path)
+
+    def load_base_model(self, weights_path):
+        if weights_path is not None:
+            base_model = models.segmentation.deeplabv3_resnet50(pretrained=False)
+            base_model.load_state_dict(torch.load(weights_path))
+        else:
+            # Load a default model if weights_path is not provided
+            base_model = models.segmentation.deeplabv3_resnet50(pretrained=True)
+        return base_model
+
     def __len__(self):
         return len(self.image_paths)
 
@@ -28,11 +41,10 @@ def __getitem__(self, index):
 
         if self.transform is not None:
             image = self.transform(image)
-            
+
         mask_transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
         mask = mask_transform(mask)
         mask = mask.to(torch.long)
-
-        # You may need to further preprocess the mask if required
-        # Example: Convert mask to tensor and perform class mapping
+
+
         return image, mask