torch.nn.functional.upsample_bilinear(input, size=None, scale_factor=None) 因为这些现在都建议使用上面的interpolate方法实现,所以就不解释了 更加复杂的例子可见:pytorch 不使用转置卷积来实现上采样
x = nn.functional.interpolate(x, scale_factor=8, mode='bilinear', align_corners=False)
torch.nn.functional.interpolate函数_人类高质量算法工程师的博客-CSDN博客blog.csdn.net/qq_35037684/article/details/119086318#:~:text=torch.nn.functional.interpolate%20%28input%2C%20size%3DNone%2C%20sc%20al%20e_fa%20ct%20or%3DNone%2C,spa%20tial%20%282D%2C%20%E5%A6%82jpg%E3%80%81...
🐛 Describe the bug The funcion torch.nn.functional.interpolate can't be used at the same time with torch.jit.script and torchvision.models.feature_extraction.create_feature_extractor (that uses torch.fx) if the destination size is an inp...
pytorchtorch.nn.functional实现插值和上采样 interpolate torch.nn.functional.interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=None)根据给定的size或scale_factor参数来对输⼊进⾏下/上采样 使⽤的插值算法取决于参数mode的设置 ⽀持⽬前的temporal(1D, 如向量数据), ...
在PyTorch中,插值方法主要通过torch.nn.functional.interpolate函数和torch.nn.Upsample类实现。PyTorch支持的插值方法包括: nearest(最近邻插值) linear(线性插值) bilinear(双线性插值) bicubic(双三次插值) trilinear(三线性插值)3. 如何在torch中使用这些插值方法 ...
torch.nn.functional.conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor Applies a 3D convolution over an input image composed of several input planes. SeeConv3dfor details and output shape. Note
optional<bool>(true);autooptions =torch::nn::functional::InterpolateFuncOptions() .size(size) .scale_factor(c10::nullopt) .mode(torch::kBicubic) .align_corners(c10::nullopt) .recompute_scale_factor(recompute_scale_factor) .antialias(false);torch::nn::functional::interpolate(input, options)...
如果您想下采样/常规调整大小,您应该使用interpolate()方法,这里的上采样方法已经不推荐使用了。 举例: import torch from torch import nn input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) input 1. 2. 3. 4. 返回:
CLASS torch.nn.UpsamplingBilinear2d(size=None, scale_factor=None) 专门用于2D数据的双线性插值算法,参数等跟上面的差不多,省略 形状: 注意:最好还是使用nn.functional.interpolate(...,mode='bilinear',align_corners=True) 举例: m = nn.UpsamplingBilinear2d(scale_factor=2) ...