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