interpolate(in_cpu, size=(osize, ), recompute_scale_factor=False, mode="nearest-exact") out_mps = F.interpolate(in_mps, size=(osize, ), recompute_scale_factor=False, mode="nearest-exact") out_cpu - out_mps.cpu() # tensor([[[0., 0., 0., 0., 0., 1., 0., 0., 0.,...
🐛 Describe the bug The following program works fine in eager mode but raises assertion fail in compile mode. import torch def fn(input): v = torch.nn.functional.interpolate(input, [11], mode="nearest") # works fine if `mode` is set to ot...
The invention refers to a process to interpolate digital video data frames received in frames form, to film-mode or normal mode frame frequency, including a frame storing, movement valuation production movement vectors, and an interpolation between stored frame and an actual frame, characterized in ...
US5068726 Nov 26, 1990 Nov 26, 1991 Sony Corporation Coding apparatus that temporally interpolates block data and selects transmission modeUS5068726 * Nov 26, 1990 Nov 26, 1991 Sony Corporation Coding apparatus that temporally interpolates block data and selects transmission mode...
🐛 Describe the bug When input tensors are 3D, torch.nn.functional.interpolate with mode='bilinear' fails with error ValueError: Anti-alias option is only supported for bilinear and bicubic modes. Apparently, the tensor is expected to be ...
nn.functional import interpolate def get_grads(dtype, size): input = torch.randn((2, 2, 1, 1), dtype=dtype, requires_grad=True).cuda() output = interpolate(input, size=size, mode='bicubic', align_corners=True) grad_outputs = [torch.ones_like(output)] grads = torch.autograd.grad([...