loss.py 文件源码

def forward(ctx, input1, input2, y, margin, size_average):
        ctx.margin = margin
        ctx.size_average = size_average
        ctx.w1 = input1.new()
        ctx.w22 = input1.new()
        ctx.w = input1.new()
        ctx.w32 = input1.new()
        ctx._outputs = input1.new()

        _idx = input1.new().byte()

        buffer = torch.mul(input1, input2)
        torch.sum(buffer, 1, out=ctx.w1, keepdim=True)

        epsilon = 1e-12
        torch.mul(input1, input1, out=buffer)
        torch.sum(buffer, 1, out=ctx.w22, keepdim=True).add_(epsilon)

        ctx._outputs.resize_as_(ctx.w22).fill_(1)
        torch.div(ctx._outputs, ctx.w22, out=ctx.w22)
        ctx.w.resize_as_(ctx.w22).copy_(ctx.w22)

        torch.mul(input2, input2, out=buffer)
        torch.sum(buffer, 1, out=ctx.w32, keepdim=True).add_(epsilon)
        torch.div(ctx._outputs, ctx.w32, out=ctx.w32)
        ctx.w.mul_(ctx.w32)
        ctx.w.sqrt_()

        torch.mul(ctx.w1, ctx.w, out=ctx._outputs)
        ctx._outputs = ctx._outputs.select(1, 0)

        torch.eq(y, -1, out=_idx)
        ctx._outputs[_idx] = ctx._outputs[_idx].add_(-ctx.margin).clamp_(min=0)
        torch.eq(y, 1, out=_idx)
        ctx._outputs[_idx] = ctx._outputs[_idx].mul_(-1).add_(1)

        output = ctx._outputs.sum()

        if ctx.size_average:
            output = output / y.size(0)

        ctx.save_for_backward(input1, input2, y)
        return input1.new((output,))