(source_path, target_path, target_path_label, evaluation_path, transforms,
batch_size=32, return_id=False, balanced=False)
| 65 | |
| 66 | |
| 67 | def get_loader_label(source_path, target_path, target_path_label, evaluation_path, transforms, |
| 68 | batch_size=32, return_id=False, balanced=False): |
| 69 | source_folder = ImageFolder(os.path.join(source_path), |
| 70 | transforms[source_path], |
| 71 | return_id=return_id) |
| 72 | target_folder_train = ImageFolder(os.path.join(target_path), |
| 73 | transform=transforms[target_path], |
| 74 | return_paths=False, return_id=return_id) |
| 75 | target_folder_label = ImageFolder(os.path.join(target_path_label), |
| 76 | transform=transforms[target_path], |
| 77 | return_paths=False, return_id=return_id) |
| 78 | eval_folder_test = ImageFolder(os.path.join(evaluation_path), |
| 79 | transform=transforms[evaluation_path], |
| 80 | return_paths=True) |
| 81 | if balanced: |
| 82 | freq = Counter(source_folder.labels) |
| 83 | class_weight = {x: 1.0 / freq[x] for x in freq} |
| 84 | source_weights = [class_weight[x] for x in source_folder.labels] |
| 85 | sampler = WeightedRandomSampler(source_weights, |
| 86 | len(source_folder.labels)) |
| 87 | print("use balanced loader") |
| 88 | source_loader = torch.utils.data.DataLoader( |
| 89 | source_folder, |
| 90 | batch_size=batch_size, |
| 91 | sampler=sampler, |
| 92 | drop_last=True, |
| 93 | num_workers=4) |
| 94 | else: |
| 95 | source_loader = torch.utils.data.DataLoader( |
| 96 | source_folder, |
| 97 | batch_size=batch_size, |
| 98 | shuffle=True, |
| 99 | drop_last=True, |
| 100 | num_workers=4) |
| 101 | |
| 102 | target_loader = torch.utils.data.DataLoader( |
| 103 | target_folder_train, |
| 104 | batch_size=batch_size, |
| 105 | shuffle=True, |
| 106 | drop_last=True, |
| 107 | num_workers=4) |
| 108 | target_loader_label = torch.utils.data.DataLoader( |
| 109 | target_folder_label, |
| 110 | batch_size=batch_size, |
| 111 | shuffle=True, |
| 112 | drop_last=True, |
| 113 | num_workers=4) |
| 114 | test_loader = torch.utils.data.DataLoader( |
| 115 | eval_folder_test, |
| 116 | batch_size=batch_size, |
| 117 | shuffle=False, |
| 118 | num_workers=4) |
| 119 | |
| 120 | return source_loader, target_loader, target_loader_label, test_loader, target_folder_train |
| 121 | |
| 122 | |
| 123 |
no test coverage detected