Open leoauri opened 8 months ago
Hi there,
An in_ratio which has some non-two factor seems to cause max for live to instantly crash upon running inference.
in_ratio
For example
class EmptyRatio(nn_tilde.Module): def __init__(self, in_channels=1, in_ratio=1, out_channels=1, out_ratio=1): super().__init__() self.out_channels = out_channels self.out_ratio = out_ratio self.in_ratio = in_ratio self.register_method( "forward", in_channels=in_channels, in_ratio=in_ratio, out_channels=out_channels, out_ratio=out_ratio, test_buffer_size=int(8192/out_ratio*in_ratio) ) @torch.jit.export def forward(self, x): b, _, l = x.shape return torch.zeros(b, self.out_channels, int(l / self.out_ratio * self.in_ratio))
these work
EmptyRatio().export_to_ts('emptyratio.ts') ... nn~ emptyratio
EmptyRatio(in_ratio=8).export_to_ts('emptyratio8.ts') ... nn~ emptyratio8
But this crashes instantly:
EmptyRatio(in_ratio=5).export_to_ts('emptyratio5.ts') ... nn~ emptyratio5
Is that crash unexpected? Is there a technical reason why it cannot work?
Example use case: run encodec decoder (input ratio of 320).
Many thanks,
Hi there,
An
in_ratio
which has some non-two factor seems to cause max for live to instantly crash upon running inference.For example
these work
But this crashes instantly:
Is that crash unexpected? Is there a technical reason why it cannot work?
Example use case: run encodec decoder (input ratio of 320).
Many thanks,