Dataset wrapping data and target tensors. Each sample will be retrieved by indexing both tensors along the first dimension. Arguments: data_tensor (Tensor): contains sample data. target_tensor (Tensor): contains sample targets (labels).
| 24 | |
| 25 | |
| 26 | class TensorDataset(Dataset): |
| 27 | """Dataset wrapping data and target tensors. |
| 28 | |
| 29 | Each sample will be retrieved by indexing both tensors along the first |
| 30 | dimension. |
| 31 | |
| 32 | Arguments: |
| 33 | data_tensor (Tensor): contains sample data. |
| 34 | target_tensor (Tensor): contains sample targets (labels). |
| 35 | """ |
| 36 | |
| 37 | def __init__(self, data_tensor, target_tensor): |
| 38 | assert data_tensor.size(0) == target_tensor.size(0) |
| 39 | self.data_tensor = data_tensor |
| 40 | self.target_tensor = target_tensor |
| 41 | |
| 42 | def __getitem__(self, index): |
| 43 | return self.data_tensor[index], self.target_tensor[index] |
| 44 | |
| 45 | def __len__(self): |
| 46 | return self.data_tensor.size(0) |
| 47 | |
| 48 | |
| 49 | class ConcatDataset(Dataset): |
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