| 104 | |
| 105 | |
| 106 | class Loader(): |
| 107 | def __init__(self, dataset, batch_size, shuffle=False, drop_last=False, num_workers=4): |
| 108 | self.loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last, num_workers=num_workers) |
| 109 | self.iterator = None |
| 110 | |
| 111 | def __iter__(self): |
| 112 | return iter(self.loader) |
| 113 | |
| 114 | def __len__(self): |
| 115 | return len(self.loader) |
| 116 | |
| 117 | def __next__(self): |
| 118 | if self.iterator is None: |
| 119 | self.iterator = iter(self.loader) |
| 120 | |
| 121 | try: |
| 122 | samples = next(self.iterator) |
| 123 | except StopIteration: |
| 124 | self.iterator = iter(self.loader) |
| 125 | samples = next(self.iterator) |
| 126 | |
| 127 | return samples |
| 128 | |
| 129 | |
| 130 | def datasetImageNet(root='./data', train=True, transform=None): |
no outgoing calls
no test coverage detected