When input tensors are 3D,torch.nn.functional.interpolatewithmode='bilinear'fails with errorValueError: Anti-alias option is only supported for bilinear and bicubic modes. Apparently, the tensor is expected to be 4D to succeed this operation, I am not sure why that is the case but if necessa...
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), spatial(2D,如jpg、png等图像数据)和volumetric(3D,如点云数据)类型...
x = nn.functional.interpolate(x, scale_factor=8, mode='bilinear', align_corners=False)
def _interpolate_helper(name, dim, interpolate_mode): @quantized_args(True, False, False) def symbolic_fn(g, input, output_size, *args): scales, align_corners = _get_interpolate_attributes(g, interpolate_mode, args) align_corners = _maybe_get_scalar(align_corners) coordinate_transformation_...
注意:最好还是使用nn.functional.interpolate(...,mode='bilinear',align_corners=True) 举例: m = nn.UpsamplingBilinear2d(scale_factor=2) m(input) 返回: tensor([[[1.0000,1.3333,1.6667,2.0000], [1.6667,2.0000,2.3333,2.6667], [2.3333,2.6667,3.0000,3.3333], [...
注意:最好还是使用nn.functional.interpolate(..., mode='bilinear', align_corners=True) 举例: m = nn.UpsamplingBilinear2d(scale_factor=2) m(input) 1. 2. 返回: tensor([[[1.0000, 1.3333, 1.6667, 2.0000], [1.6667, 2.0000, 2.3333, 2.6667], [...
pytorch中的上采样函数,用法: import torch.nn.functional as F F.interpolate(x, scale_factoir=2, mode='nearest')需要进一步了解可以查看pytorch官方文档,或者下面这篇博客: torch.nn.functional.i…
() # 使用双线性插值将图像放大两倍 scale_factor = 2 output_tensor_bilinear = F.interpolate(input_tensor, scale_factor=scale_factor, mode='bilinear', align_corners=True) # 显示原始图像和插值后的图像 plt.subplot(1, 2, 1) plt.title("Original") plt.imshow(to_img(input_tensor.squeeze(0)....
🐛 Bug torch.nn.functional.interpolate with mode='nearest' has changed in PyTorch 1.9. The previous behaviour is more logical given a scale factor so close to 1.0 should not affect the nearest pixel. To Reproduce Steps to reproduce the be...
if interpolate: # Iterpolating instead of padding x = F.interpolate(x, size=(x_copy.size(2), x_copy.size(3)), mode="bilinear", align_corners=True) else: # Padding in case the incomping volumes are of different sizes diffY = x_copy.size()[2] - x.size()[2] ...