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
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,...
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 ...
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 ...
Returns a tensor with the same size as input filled with fill_value. torch.full_like(input, fill_value) is equivalent to torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device). Parameters input (Tensor)– the size of input will determine size ...
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. ...
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Containers Module Sequential ModuleList ModuleDict ParameterList ParameterDict Convolution layers Conv1d Conv2d Conv3d ConvTranspose1d ConvTranspose2d ConvTranspose3d Unfold Fold Pooling layers MaxPool1d MaxPool2d MaxPool3d MaxUnpool1d MaxUnpool2d
69 + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. 70 + dtype (`torch.dtype`): 71 + The dtype to use for the 4D attention mask. 72 + device ...
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