kernel_size=3, padding=1会怎样改变尺寸
时间: 2023-05-30 08:06:21 浏览: 52
使用kernel_size=3和padding=1会保持输入图像的尺寸不变。这是因为padding=1将在输入图像的周围增加一个像素边框,而kernel_size=3意味着在每个像素的周围使用3x3的卷积核进行卷积操作。因此,输出图像的尺寸与输入图像的尺寸相同。
相关问题
class DnCNN(nn.Module): def __init__(self, channels, num_of_layers=17): super(DnCNN, self).__init__() kernel_size = 3 padding = 1 features = 64 layers = [] layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.ReLU(inplace=True)) for _ in range(num_of_layers-2): layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False)) layers.append(nn.BatchNorm2d(features)) layers.append(nn.ReLU(inplace=True)) layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False)) self.dncnn = nn.Sequential(*layers) def forward(self, x): out = self.dncnn(x) return out怎么改为训练集输出[16,3,50,50],评估集输出[1,3,256,256]
你需要将 DnCNN 模型的输入和输出大小调整为适合训练集和评估集的大小。可以使用 torch.nn.functional.interpolate() 函数将输入大小调整为 [16, 3, 50, 50],并在模型输出之前使用一个 nn.Upsample() 层将输出大小调整为 [1, 3, 256, 256]。
以下是修改后的代码:
```python
import torch.nn.functional as F
class DnCNN(nn.Module):
def __init__(self, channels, num_of_layers=17):
super(DnCNN, self).__init__()
kernel_size = 3
padding = 1
features = 64
layers = []
layers.append(nn.Conv2d(in_channels=channels, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
layers.append(nn.ReLU(inplace=True))
for _ in range(num_of_layers-2):
layers.append(nn.Conv2d(in_channels=features, out_channels=features, kernel_size=kernel_size, padding=padding, bias=False))
layers.append(nn.BatchNorm2d(features))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.Conv2d(in_channels=features, out_channels=channels, kernel_size=kernel_size, padding=padding, bias=False))
self.dncnn = nn.Sequential(*layers)
def forward(self, x):
# 将输入大小调整为 [16, 3, 50, 50]
x = F.interpolate(x, size=(50, 50), mode='bilinear', align_corners=False)
out = self.dncnn(x)
# 将输出大小调整为 [1, 3, 256, 256]
out = nn.Upsample(size=(256, 256), mode='bilinear', align_corners=False)(out)
return out
```
请注意,这里使用了双线性插值的方法调整了输入和输出的大小。你可以根据实际情况选择其他的插值方法。
import torch from torch import nn import d2l net = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2), nn.Flatten(), nn.Linear(9216, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(p=0.5), nn.Linear(4096, 10)),RuntimeError: mat1 and mat2 shapes cannot be multiplied (2x4096 and 9216x4096),如何调整参数
这个错误是由于 nn.Linear(9216, 4096) 和前面一些卷积层的输出形状不兼容,导致无法进行矩阵乘法运算。具体来说,nn.Linear(9216, 4096) 的输入形状是 (batch_size, 9216),而前面一些卷积层的输出形状可能是 (batch_size, h, w, c),其中 h, w, c 分别表示高度、宽度和通道数,这样的形状无法直接与 nn.Linear(9216, 4096) 进行矩阵乘法运算。
要解决这个问题,你可以在 nn.Linear(9216, 4096) 之前添加一层 nn.Flatten(),将前面卷积层的输出形状展平成二维形状。具体来说,你可以将代码修改为如下所示:
```python
import torch
from torch import nn
import d2l
net = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1), nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1), nn.ReLU(),
nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, padding=1), nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Flatten(),
nn.Linear(9216, 4096), nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096, 4096), nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(4096, 10)
)
```
希望这可以帮助你解决问题!