在用 torch.nn.Upsample 给分割 label 上采样时报错：RuntimeError: "upsample_nearest2d_out_frame" not implemented for 'Long'。
参考 [1-3]，用 [3] 给出的实现。稍微扩展一下，支持 h、w 用不同的 scale factor，并测试其与 PyTorch 的几个 upsample 类的异同，验证 [3] 的实现用 nearest 插值。
Code
linear要 3D 输入、trilinear要 5D 输入，故此两种插值法没比。
import torch
import torch.nn as nnclass UpsampleDeterministic(nn.Module):"""deterministic upsample with `nearest` interpolation"""def __init__(self, scale_factor=2):"""Input:scale_factor: int or (int, int), ratio to scale (along heigth & width)"""super(UpsampleDeterministic, self).__init__()if isinstance(scale_factor, (tuple, list)):assert len(scale_factor) == 2self.scale_h, self.scale_w = scale_factorelse:self.scale_h = self.scale_w = scale_factorassert isinstance(self.scale_h, int) and isinstance(self.scale_w, int)def forward(self, x):"""Input:x: [n, c, h, w], torch.TensorOutput:upsampled x': [n, c, h * scale_h, w * scale_w]"""return x[:, :, :, None, :, None].expand(-1, -1, -1, self.scale_h, -1, self.scale_w).reshape(x.size(0), x.size(1), x.size(2) * self.scale_h, x.size(3) * self.scale_w)# 随机数据
x = torch.rand(2, 3, 4, 4) # [n, c, h, w]
# [3] 的实现
us_det = UpsampleDeterministic((2, 3))
# pytorch 自带的几种实现
us_list = {mode: nn.Upsample(scale_factor=(2, 3), mode=mode)for mode in ('nearest', 'bilinear', 'bicubic')}
# linear: 3D
# trilinear: 5Dy_det = us_det(x)
print(y_det.size())
for us_name, us in us_list.items():y = us(x)print(us_name, y.size(), (y_det != y).sum())
输出：
torch.Size([2, 3, 8, 12])
nearest torch.Size([2, 3, 8, 12]) tensor(0)
bilinear torch.Size([2, 3, 8, 12]) tensor(507)
bicubic torch.Size([2, 3, 8, 12]) tensor(576)
可见 [3] 的实现与 nearest 结果一致。
References
- 请慎用torch.nn.Upsample
- PyTorch中模型的可复现性
- Non Deterministic Behaviour even after cudnn.deterministic = True and cudnn.benchmark=False #12207
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