Open shadowpa0327 opened 1 year ago
Hi!
Thanks for the excellent work!
I do like to ask, whether the tracker can support tracking the class which is created by inheit nn.Conv2D. Currently, I try to prune the efficientNet, but efficientNet use a class which is derive as below:
nn.Conv2D
class Conv2dDynamicSamePadding(nn.Conv2d): """ 2D Convolutions like TensorFlow, for a dynamic image size """ def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True): super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 def forward(self, x): ih, iw = x.size()[-2:] kh, kw = self.weight.size()[-2:] sh, sw = self.stride oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) if pad_h > 0 or pad_w > 0: x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
How can I make this layer been tracked? Or if you can provide me some insight? Thanks!
Hi!
Thanks for the excellent work!
I do like to ask, whether the tracker can support tracking the class which is created by inheit
nn.Conv2D
. Currently, I try to prune the efficientNet, but efficientNet use a class which is derive as below:How can I make this layer been tracked? Or if you can provide me some insight? Thanks!