torch.as_strided 是 支持fp32 torch.from_numpy 是 支持输出fp16,fp32,fp64,uint8,int8,int16,int32,int64,bool torch.frombuffer 是 支持bf16,fp16,fp32,fp64,uint8,int8,int16,int32,int64,bool torch.zeros 是 torch.zeros_like 是 支持bf16,fp16,fp32,uint8,int8,int16,int...
It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation). The parameters kernel_size, stride, padding, output_padding can either be: a single int –in which case the same value is used for the height and width ...
as_strided(size, stride, storage_offset=0)→ Tensor See torch.as_strided() atan() → Tensor See torch.atan() atan2(other)→ Tensor See torch.atan2() atan2_(other)→ Tensor In-place version of atan2() atan_() → Tensor In-place version of atan() backward(gradient=None, retain_grap...
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strided access, contiguous strides, non-contiguous strides • Storage is where the core data of the tensor is kept. It is always a 1-D array of numbers of length size, no matter the dimensionality or shape of the tensor. Keeping a 1-D storage allows us to have tensors with different...
MaxPool1d MaxPool2d MaxPool3d ZeroPad2d ConstantPad1d ConstantPad2d ConstantPad3d LeakyReLU LogSigmoid MultiheadAttention ReLU6 RReLU SELU CELU Sigmoid Softplus Softshrink Softsign Tanh Tanhshrink Threshold Non-linear activations (other)
torch.blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor torch.hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor ...
def _test_get_strided_helper(self, num_samples, window_size, window_shift, snip_edges): waveform = torch.arange(num_samples).float() output = kaldi._get_strided(waveform, window_size, window_shift, snip_edges) # from NumFrames in feature-window.cc n = window_size if snip_edges: m...
Paddle as stride op 比 torch 慢了700倍Hi! We've received your issue and please be patient to...