Unfortunately, without overarching advice about future directions in these areas, individual guidance is inferred from the existing software/hardware ecosystem, and each discipline often conducts their research independently assuming all other technologies remain fixed.
Permute by row torch how to#
Self.q = Quantizer(self.levels, config_ms.q.For decades, computer scientists have sought guidance on how to evolve architectures, languages, and programming models in order to improve application performance, efficiency, and productivity. Vis.histogram_plot.HistogramPlot('train', 'histo/enc_'.format, list(self.levels)))) Self.down = conv(Cf, Cf, kernel_size=5, stride=2)Įdsr.ResBlock(conv, Cf, kernel_size, act=nn.ReLU(True)) Mono_complex_specgram = mono_complex_specgram + \Įxpected_complex_stretch = librosa.phase_vocoder(mono_complex_specgram,Ĭomplex_stretch = complex_specgrams_stretch.numpy()Ĭomplex_stretch = complex_stretch + 1j * complex_stretchĪssert np.allclose(complex_stretch, expected_complex_stretch, atol=1e-5) Mono_complex_specgram = complex_specgrams.numpy() Index = * (complex_specgrams.dim() - 3) + * 3 Phase_advance = torch.linspace(0, np.pi * hop_length, complex_specgrams.shape, dtype=torch.float64)Ĭomplex_specgrams_stretch = F.phase_vocoder(complex_specgrams, rate=rate, phase_advance=phase_advance)Įxpected_size = list(complex_specgrams.size())Įxpected_size = int(np.ceil(expected_size / rate))Īssert complex_specgrams.dim() = complex_specgrams_stretch.dim()Īssert complex_specgrams_stretch.size() = torch.Size(expected_size) # result in bottom right values of the stretched sectrogram to notĬomplex_specgrams = complex_specgrams.type(torch.float64) # Due to cummulative sum, numerical error in using torch.float32 will Grid_left, grid_top, grid_left + grid_width, grid_top + grid_heightĭef test_phase_vocoder(complex_specgrams, rate, hop_length): Grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1) Grid_y = grid_top + grid_height * intervel
![permute by row torch permute by row torch](https://cdn.shopify.com/s/files/1/0781/4013/collections/category_banner_7_1024x1024.png)
Grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1) Grid_x = grid_left + grid_width * intervel Intervel = torch.linspace(0., 1., self.dcn_kernel).view( Grid_topleft = bxy + bwh * reg - 0.5 * bwh * torch.exp( :return: generate grids on the regressed bboxes.īxy = (previous_boxes + previous_boxes) / 2. :param reg: the regression value to previous bboxes. Regressed bboxes and generate the grids on the bboxes. """Base on the previous bboxes and regression values, we compute the Output = output.permute(0, 2, 1, 3).contiguous()ĭef gen_grid_from_reg(self, reg, previous_boxes): Output = F.grid_sample(feat.view(1, ch, h, w).expand(num_st, ch, h, w), sample_grid)Īssert output.size() = (num_st, ch, num_ed, self.align_size) Sample_grid = sample_grid / (h - 1) * 2 - 1 Sample_grid = sample_grid / (w - 1) * 2 - 1 Sample_grid = sample_grid.view(num_st, num_ed, self.align_size, 2) Sample_grid = torch.einsum("ijd,ijs->ijsd", (arr_st2ed, sample_grid)) + coord_st.view(num_st, num_ed, 1, 2).expand(num_st, num_ed, self.align_size, 2) Sample_grid = torch.linspace(0, 1, steps=self.align_size).to(feat).view(1, 1, self.align_size).expand(num_st, num_ed, self.align_size) # construct bounding boxes from junction pointsĬoord_st = coord_st.unsqueeze(1).expand(num_st, num_ed, 2)Ĭoord_ed = coord_ed.unsqueeze(0).expand(num_st, num_ed, 2) Num_st, num_ed = coord_st.size(0), coord_ed.size(0)Īssert coord_st.size(1) = 3 and coord_ed.size(1) = 3Īssert (coord_st = coord_st).all() and (coord_ed = coord_st).all() TenMask = tenOutput tenMask = 1.0 tenMask = 0.0ĭef forward(self, feat, coord_st, coord_ed): TenOutput = torch.nn.id_sample(input=tenInput, grid=(backwarp_tenGrid + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros', align_corners=True) If str(tenFlow.size()) not in backwarp_tenPartial:īackwarp_tenPartial = tenFlow.new_ones(, 1, tenFlow.shape, tenFlow.shape ]) TenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape).view(1, 1, tenFlow.shape, 1).expand(tenFlow.shape, -1, -1, tenFlow.shape)īackwarp_tenGrid = torch.cat(, 1).cuda() TenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape).view(1, 1, 1, tenFlow.shape).expand(tenFlow.shape, -1, tenFlow.shape, -1)
![permute by row torch permute by row torch](https://i.stack.imgur.com/GOueN.png)
If str(tenFlow.size()) not in backwarp_tenGrid: