如何使用 F.interpolate 调整 PyTorch 中所有 4 个维度 (NCHW) 的大小？
How to resize all 4 dimensions (NCHW) in PyTorch with F.interpolate?
我一直在尝试弄清楚如何调整张量中批次、通道、高度和宽度维度的大小。目前我可以调整 Channels、Height 和 Width 尺寸，但 Batch 尺寸保持不变。
x = torch.ones(3,4,64,64)
x = F.interpolate(x.unsqueeze(0), size=(3,4,4), mode="trilinear").squeeze(0)
x.size() # (3,3,4,4) # batch dimension has not been resized.
# I need x to be resized so that it has a size of: (1,3,4,4)
# Is this a good idea?
x = x.permute(1,0,2,3)
x = F.interpolate(x.unsqueeze(0), size=(1, x.size(2), x.size(3)), mode="trilinear").squeeze(0)
x = x.permute(1,0,2,3)
x.size() # (1,3,4,4)
我应该置换张量以调整批处理维度的大小吗？或者以某种方式遍历它？
这似乎让我可以调整批次维度的大小：
x = x.permute(1,0,2,3)
x = F.interpolate(x.unsqueeze(0), size=(1, x.size(2), x.size(3)), mode="trilinear").squeeze(0)
x = x.permute(1,0,2,3)
我一直在尝试弄清楚如何调整张量中批次、通道、高度和宽度维度的大小。目前我可以调整 Channels、Height 和 Width 尺寸，但 Batch 尺寸保持不变。
x = torch.ones(3,4,64,64)
x = F.interpolate(x.unsqueeze(0), size=(3,4,4), mode="trilinear").squeeze(0)
x.size() # (3,3,4,4) # batch dimension has not been resized.
# I need x to be resized so that it has a size of: (1,3,4,4)
# Is this a good idea?
x = x.permute(1,0,2,3)
x = F.interpolate(x.unsqueeze(0), size=(1, x.size(2), x.size(3)), mode="trilinear").squeeze(0)
x = x.permute(1,0,2,3)
x.size() # (1,3,4,4)
我应该置换张量以调整批处理维度的大小吗？或者以某种方式遍历它？
这似乎让我可以调整批次维度的大小：
x = x.permute(1,0,2,3)
x = F.interpolate(x.unsqueeze(0), size=(1, x.size(2), x.size(3)), mode="trilinear").squeeze(0)
x = x.permute(1,0,2,3)