where(mask, torch.tensor(0), data) print(filled_data) 复制代码 使用插值方法填充缺失数据:可以使用torch.interp()函数进行插值填充缺失数据。例如: data = torch.tensor([1, 2, float('nan'), 4, float('nan')]) mask = torch.isnan(data) indices = torch.arange(len(data)) interpolated_data ...
RVM支持首帧和后续帧两种推理方式 首帧:512x512 RGB 图片 + 初始化为全零的4个特征 ➡️ 前景 RGB 图片 + 分割MASK + 当前帧4个特征 后续帧:512x512 RGB 图片 + 前一帧的4个特征 ➡️ 前景 RGB 图片 + 分割MASK + 当前帧4个特征 如果采用首帧模式导出模型,模型中将会含有大量数值为零的特征,...
key_padding_mask –if provided, specified padding elements in the key will be ignored by the attention. This is an binary mask. When the value is True, the corresponding value on the attention layer will be filled with -inf. need_weights –output attn_output_weights. attn_mask –mask ...
masked_fill(mask, value)→ TensorOut-of-place version of torch.Tensor.masked_fill_()masked_select(mask)→ TensorSee torch.masked_select()matmul(tensor2)→ TensorSee torch.matmul()matrix_power(n)→ TensorSee torch.matrix_power()max(dim=None, keepdim=False) -> Tensor or (Tensor, Tensor)...
mask (ByteTensor)– the tensor containing the binary mask to index with out (Tensor, optional)– the output tensor. Example: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 复制 >>> x = torch.randn(3, 4) >>> x tensor([[ 0.3552, -2.3825, -0.8297, 0.3477], [-1.2035, 1.2252,...
key_padding_mask– if provided, specified padding elements in the key will be ignored by the attention. This is an binary mask. When the value is True, the corresponding value on the attention layer will be filled with -inf. need_weights– output attn_output_weights. ...
torch.masked_select outputs a too large tensor filled with trash when the mask is obtained from PIL #87206 Closed philip-bl opened this issue Oct 18, 2022· 4 comments Comments philip-bl commented Oct 18, 2022 🐛 Describe the bug In the following example, torch.masked_select produces ...
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forward(src,tgt,src_mask=None,tgt_mask=None,memory_mask=None,src_key_padding_mask=None,tgt_key_padding_mask=None,memory_key_padding_mask=None)[source] Take in and process masked source/target sequences. Parameters Shape: Note: [src/tgt/memory]_mask should be filled with float(‘-inf’)...
torch.tensor(data, dtype=None, device=None, requires_grad=False, pin_memory=False) → Tensor torch.sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) → Tensor torch.as_tensor(data, dtype=None, device=None) → Tensor ...