>x=torch.Tensor(3,4):zero()>x:select(2,3):fill(7)--fill column 3 with 7>print(x)007000700070[torch.DoubleTensorof dimension3x4]>y=x:transpose(1,2)--swap dimension 1 and 2>print(y)000000777000[torch.DoubleTensorof dimension4x3]>y:select(2,3):fill(8)--fill column 3 with 8>pri...
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() # 对于tensor的reshape操作通常可以分为:名字name、维度shape和类型type # view和reshape是在改变形状 # squeeze unsqueeze 增加维度和删减维度 # transpose permute 是变换维度 # expand repeat 维度拓展 images=torch.randn(4,1,28,28) # 4张通道数为1的28*28的图片 images_batch=images.view(4,28*28)...
imshow(np.transpose(npimg, (1, 2, 0))) # get some random training images dataiter = iter(trainloader)# 创建一个迭代器,用于遍历训练数据加载器 images, labels = next(dataiter)## 从迭代器中获取一批图像和对应的标签 # show images imshow(torchvision.utils.make_grid(images))# 将多张图像拼接...
IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as `resize_` / `resize_as_` / `set_` / `transpose_`) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an...
X = torch.reshape(N, D, H * W) # Assume X has shape N*D*H*WX = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W) # Bilinear poolingassert X.size == (N, D, D)X = torch.reshape(X, (N, D * D))X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5) # Si...
X = torch.reshape(N, D, H * W)# Assume X has shape N*D*H*WX = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W)# Bilinear poolingassert X.size() == (N, D, D) X = torch.reshape(X, (N, D * D)) X = torch.sign(X) * torch.sqrt(torch.abs(X) + 1e-5)# ...
transpose(zeros_set.repeat(ones_set.shape[1], 1), 0, 1).clone() repeat_zeros = repeat_zeros_set.view(1, -1) difference_val = -(repeat_ones - repeat_zeros) exp_val = torch.exp(difference_val) exp_loss = torch.sum(exp_val) normalized_loss = exp_loss / (zeros_set.shape[1] ...
transpose(1, 2).transpose(0, 1) return data # transformer: frame splitter Example #7Source File: modules.py From melgan-neurips with MIT License 6 votes def forward(self, audio): p = (self.n_fft - self.hop_length) // 2 audio = F.pad(audio, (p, p), "reflect").squeeze(1) ...
Default: 1 Examples:: >>> inputs = torch.randn(20, 16, 50) >>> weights = torch.randn(16, 33, 5) >>> F.conv_transpose1d(inputs, weights) """, ) conv_transpose2d = _add_docstr( torch.conv_transpose2d, r""" conv_transpose2d(input, weight, bias=None, stride=1, paddin...