请解释copy-padding
时间: 2023-07-29 09:05:43 浏览: 38
Copy-padding是一种数据填充技术,用于在序列到序列(seq2seq)模型中处理可变长度的输入和输出序列。在处理时,我们需要将所有输入序列和输出序列填充到相同的长度,以便它们可以被一起处理。在copy-padding中,我们使用一个特殊的填充符号来填充输入序列或输出序列中的空白位置,同时将原始序列中的实际值复制到填充后的位置上,这样可以保留原始序列中的信息并保持序列的顺序不变。在处理填充后的序列时,我们可以通过使用掩码(mask)来忽略填充符号,只考虑原始序列中的实际值,从而防止填充符号对模型的学习产生干扰。
相关问题
add padding of 0 for interpolation window 4x4 and translation [-1,1]
To add padding of 0 for an interpolation window of 4x4 and translation [-1,1], you can use the following code snippet in MATLAB:
```
% Define the interpolation window size and translation
win_size = 4;
translation = [-1, 1];
% Create a padding matrix with zeros
padding = zeros(win_size);
% Compute the indices of the padded region
x_min = win_size/2 - translation(1);
x_max = x_min + win_size - 1;
y_min = win_size/2 - translation(2);
y_max = y_min + win_size - 1;
% Copy the original region to the padded region
padding(x_min:x_max, y_min:y_max) = original_region;
```
In this code snippet, we first define the size of the interpolation window and the translation. We then create a padding matrix with zeros using the "zeros" function in MATLAB. We then compute the indices of the padded region based on the translation and the interpolation window size. Finally, we copy the original region to the padded region by indexing into the "padding" matrix and assigning the values from the "original_region" matrix.
请给我一个fcn-32s的完整代码
好的,以下是一个fcn-32s的完整代码示例,使用的是PyTorch框架:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
class FCN32s(nn.Module):
def __init__(self, num_classes):
super(FCN32s, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=100)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.relu2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.relu4 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.relu5 = nn.ReLU(inplace=True)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu6 = nn.ReLU(inplace=True)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.relu7 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.relu8 = nn.ReLU(inplace=True)
self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu9 = nn.ReLU(inplace=True)
self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu10 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu11 = nn.ReLU(inplace=True)
self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu12 = nn.ReLU(inplace=True)
self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.relu13 = nn.ReLU(inplace=True)
self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.fc14 = nn.Conv2d(512, 4096, kernel_size=7)
self.relu14 = nn.ReLU(inplace=True)
self.drop14 = nn.Dropout2d()
self.fc15 = nn.Conv2d(4096, 4096, kernel_size=1)
self.relu15 = nn.ReLU(inplace=True)
self.drop15 = nn.Dropout2d()
self.score_fr = nn.Conv2d(4096, num_classes, kernel_size=1)
self.upscore = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, stride=32, bias=False)
self._initialize_weights()
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool1(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.pool2(x)
x = self.conv5(x)
x = self.relu5(x)
x = self.conv6(x)
x = self.relu6(x)
x = self.conv7(x)
x = self.relu7(x)
x = self.pool3(x)
x = self.conv8(x)
x = self.relu8(x)
x = self.conv9(x)
x = self.relu9(x)
x = self.conv10(x)
x = self.relu10(x)
x = self.pool4(x)
x = self.conv11(x)
x = self.relu11(x)
x = self.conv12(x)
x = self.relu12(x)
x = self.conv13(x)
x = self.relu13(x)
x = self.pool5(x)
x = self.fc14(x)
x = self.relu14(x)
x = self.drop14(x)
x = self.fc15(x)
x = self.relu15(x)
x = self.drop15(x)
x = self.score_fr(x)
x = self.upscore(x)
x = x[:, :, 19:19+x.size()[2], 19:19+x.size()[3]].contiguous()
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.zero_()
if m.bias is not None:
m.bias.data.zero_()
if isinstance(m, nn.ConvTranspose2d):
assert m.kernel_size[0] == m.kernel_size[1]
initial_weight = get_upsampling_weight(m.in_channels, m.out_channels, m.kernel_size[0])
m.weight.data.copy_(initial_weight)
def get_upsampling_weight(in_channels, out_channels, kernel_size):
factor = (kernel_size + 1) // 2
if kernel_size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = torch.ogrid[:kernel_size, :kernel_size]
filter = (1 - abs(og[0] - center) / factor) * (1 - abs(og[1] - center) / factor)
weight = torch.zeros((in_channels, out_channels, kernel_size, kernel_size))
weight[range(in_channels), range(out_channels), :, :] = filter
return weight
```
这个代码实现了FCN32s模型,可以输入一张图片,输出对应的语义分割结果。