torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) 在由几个输入平面组成的输入图像上应用二维转置卷积,有时也称为“去卷积”。 有关详细信息和输出形状,查看ConvTranspose2d。
torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor source 在由几个输入平面组成的输入图像上应用2D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ConvTranspose2d。
>>> 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_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”. SeeConvTranspose2dfor...
5.3,nn.ConvTranspose2d 反卷积 参考资料 授人以鱼不如授人以渔,原汁原味的知识才更富有精华,本文只是对张量基本操作知识的理解和学习笔记,看完之后,想要更深入理解,建议去 pytorch 官方网站,查阅相关函数和操作,英文版在这里,中文版在这里。本文的代码是在pytorch1.7版本上测试的,其他版本一般也没问题。
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像上应用二维转置卷积,有时也称为“去卷积”。 有关详细信息和输出形状,请参阅ConvTranspose2d。
例子：在由多个输入平面组成的输入图像上应用二维转置卷积，又称“去卷积”。详情及输出形状请参阅ConvTranspose2d。参数：输入张量形状(minibatch x in_channels x iH x iW)、过滤器形状(in_channels x out_channels x kH x kW)、可选偏置(out_channels)、步长可以是单个数字或元组(sh x sw)、...
🐛 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...
conv_transpose2d¶ torch.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)→ Tensor¶ Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”...
对于没有通过Class包装的op,比如functional.conv2d或者functional.linear,无能为力 其实最重要的就是缺乏自动化,啥都要自己写: importtorch # define a floating point model where some layers could be statically quantized classM(torch.nn.Module):