fixed dataset structure

NeuralNetwork/neural_network_learning.py

@@ -1,5 +1,5 @@
 import glob
-from src.torchvision_resize_dataset import combined_dataset, letters_path, package_path
+from src.torchvision_resize_dataset import combined_dataset, images_path, classes
 import src.data_model
 from torch.optim import Adam
 import torch 
@@ -13,7 +13,6 @@ train_loader = DataLoader(
     batch_size=256,     # accuracy
     shuffle=True    # rand order
 )
-classes = ["package", "list"]
 
 model = src.data_model.DataModel(num_objects=2).to(device)
 
@@ -23,7 +22,7 @@ optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
 criterion = nn.CrossEntropyLoss()
 
 num_epochs = 10
-train_size = len(glob.glob(letters_path, '*.jpg')) + len(glob.glob(package_path, '*.png'))
+train_size = len(glob.glob(images_path, '*.jpg'))
 
 go_to_accuracy = 0.0
 for epoch in range(num_epochs):

NeuralNetwork/src/torchvision_resize_dataset.py

@@ -1,3 +1,4 @@
+import pathlib
 import torchvision.transforms as transforms
 from torchvision.datasets import ImageFolder
 from torch.utils.data import ConcatDataset
@@ -10,12 +11,20 @@ transform = transforms.Compose([
     transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
 ])
 
-letters_path = './letters'
-package_path = './package'
+letters_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/letters'
+package_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/package'
+images_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images'
 
 # # Load images from folders
-letter_folder = ImageFolder(letters_path, transform=transform)
-package_folder = ImageFolder(package_path, transform=transform)
+# letter_folder = ImageFolder(letters_path, transform=transform)
+# package_folder = ImageFolder(package_path, transform=transform)
 
 # Combine the both datasets into a single dataset
-combined_dataset = ConcatDataset([letter_folder, package_folder])
+#combined_dataset = ConcatDataset([letter_folder, package_folder])
+combined_dataset = ImageFolder(images_path, transform=transform)
+
+#image classes
+path=pathlib.Path(images_path)
+classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
+
+# print(classes)