View in context As the Palmer, lighted by a domestic with a torch, past through the intricate combination of apartments of this large and irregular mansion, the cupbearer coming behind him whispered in his ear, that if he had no objection to a cup of good mead in his apartment, there we...
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) ...
view_as_real()仅支持具有complex dtypes的张量。 例子: >>>x=torch.randn(4, dtype=torch.cfloat)>>>x tensor([(0.4737-0.3839j), (-0.2098-0.6699j), (0.3470-0.9451j), (-0.5174-1.3136j)])>>>torch.view_as_real(x) tensor([[0.4737,-0.3839], [-0.2098,-0.6699], [0.3470,-0.9451], [-0...
Test code: importtorchself=torch.randn([1,1,1,1],dtype=torch.complex64)other=torch.randn([1,1,1,1,2],dtype=torch.float64)self.view_as(other) Error log: Traceback (most recent call last): File "/home/yonghyeon/pytorch_err_case_py/view_as.py", line 6, in <module> self.view_...
flashlight (redirected fromElectric torch) Thesaurus Encyclopedia flash·light (flăsh′līt′) n. 1.A small portable lamp usually powered by batteries. 2.A brief brilliant flood of light from a photographic lamp. 3.A bright light, as of a signal lamp, that flashes at regular intervals. ...
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view(batch_size, -1) if data_range is None: # by default use max, same as fastmri data_range = gt.max(dim=1)[0]# - gt.min(dim=1)[0] mse_err = (abs(gt - pred) ** 2).mean(1) psnr_val = 10 * torch.log10(data_range ** 2 / mse_err) if reduce: return psnr_val...
#include <c10/util/complex.h> #include <functional> #include <iostream> #include <string> #include <utility> namespace torch { namespace jit { // clang-format off // TreeView provides a statically-typed way to traverse the tree, which should ...
is_complex()) else x.double(), memory_format=torch.legacy_contiguous_format) if gen_non_contig_grad_outputs: y = torch.testing.make_non_contiguous(y) return y.requires_grad_() outputs = _as_tuple(func(*tupled_inputs)) tupled_grad_outputs = tuple(randn_like(x) for x in outputs) ...
div_(complex_norm(angles).add_(1e-16).unsqueeze(-1).expand_as(angles)) # Return the final phase estimates waveform = istft(specgram * angles, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, length=length) # unpack batch waveform = waveform.view(shape[:-2...