| from functools import reduce |
| import torch |
| import torch._utils |
| from ..function import Function |
|
|
|
|
| class Type(Function): |
|
|
| @staticmethod |
| def forward(ctx, i, dest_type): |
| ctx.input_type = type(i) |
| ctx.input_device = -1 if not i.is_cuda else i.get_device() |
| return i.type(dest_type) |
|
|
| @staticmethod |
| def backward(ctx, grad_output): |
| if ctx.input_device == -1: |
| return grad_output.type(ctx.input_type), None |
| else: |
| with torch.cuda.device(ctx.input_device): |
| return grad_output.type(ctx.input_type), None |
|
|
|
|
| |
| class Resize(Function): |
|
|
| @staticmethod |
| def forward(ctx, tensor, sizes): |
| ctx.sizes = sizes |
| ctx.numel = reduce(lambda x, y: x * y, sizes, 1) |
| if tensor.numel() != ctx.numel: |
| raise RuntimeError(("requested resize to {} ({} elements in total), " |
| "but the given tensor has a size of {} ({} elements). " |
| "autograd's resize can only change the shape of a given " |
| "tensor, while preserving the number of elements. ").format( |
| 'x'.join(map(str, sizes)), ctx.numel, |
| 'x'.join(map(str, tensor.size())), tensor.numel())) |
| ctx.input_sizes = tensor.size() |
| if tensor.is_quantized: |
| tensor.copy_(tensor) |
| return tensor.contiguous().view(*sizes) |
| if tensor.is_contiguous(): |
| result = tensor.new(tensor).contiguous().view(*sizes) |
| return result |
| else: |
| return tensor.contiguous().view(*sizes) |
|
|
| @staticmethod |
| def backward(ctx, grad_output): |
| assert grad_output.numel() == ctx.numel |
| return grad_output.contiguous().view(ctx.input_sizes), None |
|
|
