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‎burn-book/src/building-blocks/tensor.md‎

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@@ -131,55 +131,56 @@ for the sake of simplicity, we ignore type signatures. For more details, refer t
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Those operations are available for all tensor kinds: `Int`, `Float`, and `Bool`.
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| Burn | PyTorch Equivalent |
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|---------------------------------------------|---------------------------------------------------------------------------|
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| `Tensor::cat(tensors, dim)` | `torch.cat(tensors, dim)` |
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| `Tensor::empty(shape, device)` | `torch.empty(shape, device=device)` |
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| `Tensor::from_primitive(primitive)` | N/A |
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| `Tensor::stack(tensors, dim)` | `torch.stack(tensors, dim)` |
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| `tensor.all()` | `tensor.all()` |
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| `tensor.all_dim(dim)` | `tensor.all(dim)` |
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| `tensor.any()` | `tensor.any()` |
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| `tensor.any_dim(dim)` | `tensor.any(dim)` |
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| `tensor.chunk(num_chunks, dim)` | `tensor.chunk(num_chunks, dim)` |
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| `tensor.split(split_size, dim)` | `tensor.split(split_size, dim)` |
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| `tensor.split_with_sizes(split_sizes, dim)` | `tensor.split([split_sizes], dim)` |
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| `tensor.device()` | `tensor.device` |
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| `tensor.dtype()` | `tensor.dtype` |
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| `tensor.dims()` | `tensor.size()` |
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| `tensor.equal(other)` | `x == y` |
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| `tensor.expand(shape)` | `tensor.expand(shape)` |
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| `tensor.flatten(start_dim, end_dim)` | `tensor.flatten(start_dim, end_dim)` |
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| `tensor.flip(axes)` | `tensor.flip(axes)` |
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| `tensor.into_data()` | N/A |
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| `tensor.into_primitive()` | N/A |
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| `tensor.into_scalar()` | `tensor.item()` |
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| `tensor.narrow(dim, start, length)` | `tensor.narrow(dim, start, length)` |
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| `tensor.not_equal(other)` | `x != y` |
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| `tensor.permute(axes)` | `tensor.permute(axes)` |
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| `tensor.movedim(src, dst)` | `tensor.movedim(src, dst)` |
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| `tensor.repeat_dim(dim, times)` | `tensor.repeat(*[times if i == dim else 1 for i in range(tensor.dim())])` |
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| `tensor.repeat(sizes)` | `tensor.repeat(sizes)` |
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| `tensor.reshape(shape)` | `tensor.view(shape)` |
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| `tensor.roll(shfts, dims)` | `tensor.roll(shifts, dims)` |
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| `tensor.roll_dim(shift, dim)` | `tensor.roll([shift], [dim])` |
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| `tensor.select(dim, indices)` | `tensor.index_select(dim, indices)` |
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| `tensor.select_assign(dim, indices, values)`| N/A |
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| `tensor.shape()` | `tensor.shape` |
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| `tensor.slice(s![range;step])` | `tensor[(*ranges,)]` or `tensor[start:end:step]` |
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| `tensor.slice_assign(ranges, values)` | `tensor[(*ranges,)] = values` |
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| `tensor.slice_fill(ranges, value)` | `tensor[(*ranges,)] = value` |
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| `tensor.slice_dim(dim, range)` | N/A |
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| `tensor.squeeze(dim)` | `tensor.squeeze(dim)` |
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| `tensor.swap_dims(dim1, dim2)` | `tensor.transpose(dim1, dim2)` |
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| `tensor.take(dim, indices)` | `numpy.take(tensor, indices, dim)` |
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| `tensor.to_data()` | N/A |
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| `tensor.to_device(device)` | `tensor.to(device)` |
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| `tensor.transpose()` | `tensor.T` |
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| `tensor.t()` | `tensor.T` |
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| `tensor.unsqueeze()` | `tensor.unsqueeze(0)` |
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| `tensor.unsqueeze_dim(dim)` | `tensor.unsqueeze(dim)` |
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| `tensor.unsqueeze_dims(dims)` | N/A |
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| Burn | PyTorch Equivalent |
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|----------------------------------------------|---------------------------------------------------------------------------|
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| `Tensor::cat(tensors, dim)` | `torch.cat(tensors, dim)` |
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| `Tensor::empty(shape, device)` | `torch.empty(shape, device=device)` |
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| `Tensor::from_primitive(primitive)` | N/A |
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| `Tensor::stack(tensors, dim)` | `torch.stack(tensors, dim)` |
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| `tensor.all()` | `tensor.all()` |
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| `tensor.all_dim(dim)` | `tensor.all(dim)` |
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| `tensor.any()` | `tensor.any()` |
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| `tensor.any_dim(dim)` | `tensor.any(dim)` |
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| `tensor.chunk(num_chunks, dim)` | `tensor.chunk(num_chunks, dim)` |
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| `tensor.split(split_size, dim)` | `tensor.split(split_size, dim)` |
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| `tensor.split_with_sizes(split_sizes, dim)` | `tensor.split([split_sizes], dim)` |
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| `tensor.device()` | `tensor.device` |
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| `tensor.dtype()` | `tensor.dtype` |
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| `tensor.dims()` | `tensor.size()` |
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| `tensor.equal(other)` | `x == y` |
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| `tensor.expand(shape)` | `tensor.expand(shape)` |
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| `tensor.flatten(start_dim, end_dim)` | `tensor.flatten(start_dim, end_dim)` |
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| `tensor.flip(axes)` | `tensor.flip(axes)` |
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| `tensor.into_data()` | N/A |
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| `tensor.into_primitive()` | N/A |
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| `tensor.into_scalar()` | `tensor.item()` |
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| `tensor.narrow(dim, start, length)` | `tensor.narrow(dim, start, length)` |
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| `tensor.not_equal(other)` | `x != y` |
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| `tensor.permute(axes)` | `tensor.permute(axes)` |
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| `tensor.movedim(src, dst)` | `tensor.movedim(src, dst)` |
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| `tensor.repeat_dim(dim, times)` | `tensor.repeat(*[times if i == dim else 1 for i in range(tensor.dim())])` |
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| `tensor.repeat(sizes)` | `tensor.repeat(sizes)` |
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| `tensor.reshape(shape)` | `tensor.view(shape)` |
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| `tensor.roll(shfts, dims)` | `tensor.roll(shifts, dims)` |
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| `tensor.roll_dim(shift, dim)` | `tensor.roll([shift], [dim])` |
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| `tensor.select(dim, indices)` | `tensor.index_select(dim, indices)` |
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| `tensor.select_assign(dim, indices, values)` | N/A |
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| `tensor.shape()` | `tensor.shape` |
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| `tensor.slice(s![range;step])` | `tensor[(*ranges,)]` or `tensor[start:end:step]` |
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| `tensor.slice_assign(ranges, values)` | `tensor[(*ranges,)] = values` |
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| `tensor.slice_fill(ranges, value)` | `tensor[(*ranges,)] = value` |
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| `tensor.slice_dim(dim, range)` | N/A |
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| `tensor.squeeze(dim)` | `tensor.squeeze(dim)` |
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| `tensor.swap_dims(dim1, dim2)` | `tensor.transpose(dim1, dim2)` |
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| `tensor.take(dim, indices)` | `numpy.take(tensor, indices, dim)` |
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| `tensor.to_data()` | N/A |
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| `tensor.to_device(device)` | `tensor.to(device)` |
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| `tensor.transpose()` | `tensor.T` |
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| `tensor.t()` | `tensor.T` |
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| `tensor.unfold(dim, size, step)` | `tensor.unfold(dim, size, step)` |
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| `tensor.unsqueeze()` | `tensor.unsqueeze(0)` |
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| `tensor.unsqueeze_dim(dim)` | `tensor.unsqueeze(dim)` |
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| `tensor.unsqueeze_dims(dims)` | N/A |
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### Numeric Operations
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