torch.nn.functional.conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)→ Tensorsource在由几个输入平面组成的输入图像上应用1D转置卷积,有时也被称为去卷积。 有关详细信息和输出形状,参考ConvTranspose1d。
🐛 Describe the bug Call alone import torch import torch.nn as nn conv_transpose = nn.ConvTranspose2d( in_channels=3, out_channels=4, kernel_size=[1, 1], stride=[1, 1], padding=[0, 0], output_padding=[0, 0], dilation=[7, 0], groups=1, bia...
ConvTranspose1dclass torch.nn.ConvTranspose1d(in_channels: int, out_channels: int, kernel_size: Union[T, Tuple[T]], stride: Union[T, Tuple[T]] = 1, padding: Union[T, Tuple[T]] = 0, output_padding: Union[T, Tuple[T]] = 0, groups: int = 1, bias: bool = True, dilation: ...
>>> F.conv3d(inputs, filters) torch.nn.functional.conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1) torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1) 在由几个输入平面组成的输入图...
torch.nn.functional.conv_transpose1d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1) 在由几个输入平面组成的输入图像上应用1D转置卷积,有时也被称为去卷积。 有关详细信息和输出形状,参考ConvTranspose1d。 参数:
nn.ConvTranspose1d:一维卷积转置层,数值不可逆,只是维度变换,转置卷积也是卷积 nn.ConvTranspose2d:二维卷积转置层,俗称反卷积层。并非卷积的逆操作,但在卷积核相同的情况下,当其输入尺寸是卷积操作输出尺寸的情况下,卷积转置的输出尺寸恰好是卷积操作的输入尺寸。在语义分割中可用于上采样。其实反卷积层也是卷积,数值...
print(model.conv) 输出: Conv2d(10, 20, kernel_size=(4, 4), stride=(1, 1)) children() Returns an iterator over immediate children modules. 返回当前模型 子模块的迭代器。 import torch.nn as nn class Model(nn.Module): def __init__(self): ...
dconv1 = nn.ConvTranspose1d(1, 1, kernel_size=3, stride=3, padding=1, output_padding=1) x = torch.randn(16, 1, 8) print(x.size()) # torch.Size([16, 1, 8]) output = dconv1(x) print(output.shape) # torch.Size([16, 1, 23]) (9) nn.ConvTranspose2d 二维转置卷积神经网...
1)torch.nn.Conv1d该软件包将用于对由多个输入平面组成的输入信号进行一维卷积。 2)torch.nn.Conv2d该软件包将用于在由多个输入平面组成的输入信号上应用2D卷积。 3)torch.nn.Conv3d该软件包将用于在由多个输入平面组成的输入信号上应用3D卷积。 4)torch.nn.ConvTranspose1d该软件包将用于在由多个输入平面组成的...
Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called “deconvolution”. SeeConvTranspose1dfor details and output shape. Note In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic ...