conver asl onnx weight to tensorrt face problem about asl HOT 2 CLOSED

maybe a newer version of tensroRT and ONNX will support this ?

anyway, your problem is that current anti-aliasing layer uses 'reflect' padding (this is what the original article suggested):

    def forward(self, input):
        input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
        return F.conv2d(input_pad, self.filt, stride=self.stride, padding=0, groups=input.shape[1])

you can use an implementation of anti-aliasing without padding:

class AntiAliasDownsampleLayerD(nn.Module):
    def __init__(self, channels):
        super(AntiAliasDownsampleLayerD, self).__init__()
        self.channels = channels
        a = torch.tensor([1., 2., 1.])

        filt = (a[:, None] * a[None, :]).clone().detach()
        filt = filt / torch.sum(filt)
        self.filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1)).cuda().half()

    def forward(self, input):
        if input.type() != self.filt.type():
            self.filt = self.filt.to(device=input.device, dtype=input.dtype)
        return F.conv2d(input, self.filt, stride=2, padding=1, groups=input.shape[1])

notice that this change is not bit-accurate, so you will need to retrain your models. however, you should reach the same accuracy, the 'reflect' padding doesn't contribute a lot

from asl.

maybe a newer version of tensroRT and ONNX will support this ?

anyway, your problem is that current anti-aliasing layer uses 'reflect' padding (this is what the original article suggested):

    def forward(self, input):
        input_pad = F.pad(input, (1, 1, 1, 1), 'reflect')
        return F.conv2d(input_pad, self.filt, stride=self.stride, padding=0, groups=input.shape[1])

you can use an implementation of anti-aliasing without padding:

class AntiAliasDownsampleLayerD(nn.Module):
    def __init__(self, channels):
        super(AntiAliasDownsampleLayerD, self).__init__()
        self.channels = channels
        a = torch.tensor([1., 2., 1.])

        filt = (a[:, None] * a[None, :]).clone().detach()
        filt = filt / torch.sum(filt)
        self.filt = filt[None, None, :, :].repeat((self.channels, 1, 1, 1)).cuda().half()

    def forward(self, input):
        if input.type() != self.filt.type():
            self.filt = self.filt.to(device=input.device, dtype=input.dtype)
        return F.conv2d(input, self.filt, stride=2, padding=1, groups=input.shape[1])

notice that this change is not bit-accurate, so you will need to retrain your models. however, you should reach the same accuracy, the 'reflect' padding doesn't contribute a lot

It work! Thank you very much.

from asl.