Проблема в том, что dataloader возвращает неправильный класс для соответствующего изображения?
например, если я печатаю class_to_idx из train_loader, когда размер пакета равен 1, я ожидал получить один класс на пакет, но в настоящее время он возвращает все классы, что составляет 15 классов на изображение.
В этом случае классы представляют собой класс папок (все изображения в одной папке принадлежат одному классу)
фрагмент здесь: (это функция для возврата класса из имени папки dir)
import os
def find_classes(dir): # Finds the class folders in a dataset, dir (string): Root directory path.
classes = [d.name for d in os.scandir(dir) if d.is_dir()]
classes.sort()
class_to_idx = {classes[i]: i for i in range(len(classes))}
return classes, class_to_idx
вот основной фрагмент для создания пользовательского набора данных и кодировщика данных
def main ():
class CustomDataset(Dataset):
def __init__(self, image_paths, classes, class_to_id):
self.image_paths = image_paths
self.transforms = transforms.ToTensor()
classes, class_to_id = find_classes('D:/Neda/Echo_View_Classification/avi_images/')
self.classes = classes
self.class_to_idx = class_to_idx
def __getitem__(self, index):
image = Image.open(self.image_paths[index])
t_image = image.convert('L')
t_image = self.transforms(t_image)
class_to_idx = self.class_to_idx
return t_image, class_to_idx, self.image_paths[index]
def __len__(self):
return len(self.image_paths)
folder_data = glob.glob("D:\\Neda\\Echo_View_Classification\\avi_images\\*\\*.png") # no augmnetation
#numpy.savetxt('distribution_class.csv', numpy.c_[folder_data], fmt=['%s'], comments='', delimiter = ",")
#split these path using a certain percentage
len_data = len(folder_data)
print("count of dataset: ", len_data)
split_1 = int(0.6 * len(folder_data))
split_2 = int(0.8 * len(folder_data))
folder_data.sort()
train_image_paths = folder_data[:split_1]
print("count of train images is: ", len(train_image_paths))
numpy.savetxt('im_training_path_1.csv', numpy.c_[train_image_paths], fmt=['%s'], comments='', delimiter = ",")
valid_image_paths = folder_data[split_1:split_2]
print("count of validation image is: ", len(valid_image_paths))
numpy.savetxt('im_valid_path_1.csv', numpy.c_[valid_image_paths], fmt=['%s'], comments='', delimiter = ",")
test_image_paths = folder_data[split_2:]
print("count of test images is: ", len(test_image_paths))
numpy.savetxt('im_testing_path_1.csv', numpy.c_[test_image_paths], fmt=['%s'], comments='', delimiter = ",")
classes = ['1_PLAX_1_PLAX_full',
'1_PLAX_2_PLAX_valves',
'1_PLAX_4_PLAX_TV',
'2_PSAX_1_PSAX_AV',
'2_PSAX_2_PSAX_LV',
'3_Apical_1_MV_LA_IAS',
'3_Apical_2_A2CH',
'3_Apical_3_A3CH',
'3_Apical_5_A5CH',
'4_A4CH_1_A4CH_LV',
'4_A4CH_2_A4CH_RV',
'4_Subcostal_1_Subcostal_heart',
'4_Subcostal_2_Subcostal_IVC',
'root_5_Suprasternal',
'root_6_OTHER']
class_to_idx = {'1_PLAX_1_PLAX_full': 0,
'1_PLAX_2_PLAX_valves': 1,
'1_PLAX_4_PLAX_TV': 2,
'2_PSAX_1_PSAX_AV': 3,
'2_PSAX_2_PSAX_LV': 4,
'3_Apical_1_MV_LA_IAS': 5,
'3_Apical_2_A2CH': 6,
'3_Apical_3_A3CH': 7,
'3_Apical_5_A5CH': 8,
'4_A4CH_1_A4CH_LV': 9,
'4_A4CH_2_A4CH_RV': 10,
'4_Subcostal_1_Subcostal_heart': 11,
'4_Subcostal_2_Subcostal_IVC': 12,
'root_5_Suprasternal': 13,
'root_6_OTHER': 14}
train_dataset = CustomDataset(train_image_paths, class_to_idx, classes)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=0)
valid_dataset = CustomDataset(valid_image_paths, class_to_idx, classes)
valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=0)
test_dataset = CustomDataset(test_image_paths, class_to_idx, classes)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)
dataLoaders = {
'train': train_loader,
'valid': valid_loader,
'test': test_loader,
}