padding-dilation * (kernel_size - 1) - padding 零填充将添加到输入中每个维度的两侧。可以是单个数字或元组 (padW,) 。默认值:0 output_padding-添加到输出形状中每个维度的一侧的附加大小。可以是单个数字或元组 (out_padW) 。默认值:0 groups-将输入分成组, 应该可以被组数整除。默认值:1 dilation-内核...
2 原理补充 如果输入尺寸为size_input,输出为size_output,反卷积核大小是k*k,步长为stride,out_padding 表示是对反卷积后的特征图补零(默认为0)。 那么ConvTranspose1d输出尺寸大小计算公式为: size_output = (size_input - 1)stride + k - 2padding + outpadding 3、解决方案发布...
conv1 = nn.Conv1d(1,2,3, padding=1) conv2 = nn.Conv1d(in_channels=2, out_channels=4, kernel_size=3, padding=1)#转置卷积dconv1 = nn.ConvTranspose1d(4,1, kernel_size=3, stride=2, padding=1, output_padding=1) x = torch.randn(16,1,8)print(x.size()) x1 = conv1(x) x...
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: Union[T, Tuple[T]] = 1,...
🐛 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...
idea_part = F.conv_transpose1d(x, weight_B,None, stride=self.stride, padding=0, dilation=self.dilation, groups=2)ifself.output_padding: real_part = F.pad(real_part, (self.output_padding, self.output_padding),'reflect') idea_part = F.pad(idea_part, (self.output_padding, self.output...
import torch from torch import nn import torch.nn.functional as F conv1 = nn.Conv1d(1, 2, 3, padding=1) conv2 = nn.Conv1d(in_channels=2, out_channels=4, kernel_size=3, padding=1) #转置卷积 dconv1 = nn.ConvTranspose1d(4, 1, kernel_size=3, stride=2, padding=1, output_pad...
= 'zeros': # raise ValueError('Only `zeros` padding mode is supported for ConvTranspose1d') output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size) return F.conv_transpose1d( input, self.weight, self.bias, self.stride, self.padding, output...
对公式的个人理解: 反卷积实际上,相当于先用0插值再对插值后的图像做常规卷积操作。从上述公式中可以看出,ConvTranspose2d中的参数stride参数相当于F.upsample函数中的放缩因子scale。 代入实例以供理解:现有width=60的input,欲反卷积得到output的width=240。即:240 = (60 - 1)* stride + kernalsize... ...
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