WAKE_REASON_UNFOLD_DEVICE
时间: 2024-03-12 08:42:20 浏览: 13
WAKE_REASON_UNFOLD_DEVICE是指在折叠设备上展开时唤醒设备的原因。当折叠设备处于折叠状态时,它可能会进入休眠模式以节省电量。当用户展开设备时,设备会被唤醒以响应用户的操作。
相关问题:
1. 什么是折叠设备?
2. 折叠设备的休眠模式是什么?
3. 除了WAKE_REASON_UNFOLD_DEVICE,还有哪些唤醒设备的原因?
相关问题
swin_transformer代码
Swin Transformer是2021年提出的一种新型Transformer模型,其在图像分类、物体检测等任务上表现优异。以下是Swin Transformer的代码实现:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class SwinBlock(nn.Module):
def __init__(self, dim, num_heads, window_size, shift_size):
super(SwinBlock, self).__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = nn.MultiheadAttention(dim, num_heads)
self.norm2 = nn.LayerNorm(dim)
self.mlp = nn.Sequential(
nn.Linear(dim, dim * 4),
nn.GELU(),
nn.Linear(dim * 4, dim)
)
self.window_size = window_size
self.shift_size = shift_size
def forward(self, x):
# Shift windows and flatten them
n, c, h, w = x.shape
unfold = nn.Unfold(kernel_size=(self.window_size, self.window_size),
stride=(self.shift_size, self.shift_size))
windows = unfold(x).view(n, c, -1, self.window_size * self.window_size).transpose(1, 2)
# Attention
residual1 = windows
windows = self.norm1(windows)
windows, _ = self.attn(windows, windows, windows)
windows = residual1 + windows
# MLP
residual2 = windows
windows = self.norm2(windows)
windows = self.mlp(windows)
windows = residual2 + windows
# Reshape and reassemble to original shape
fold = nn.Fold(output_size=(h, w), kernel_size=(self.window_size, self.window_size),
stride=(self.shift_size, self.shift_size))
x = fold(windows.transpose(1, 2).contiguous().view(n, -1, c))
return x
class SwinTransformer(nn.Module):
def __init__(self, in_channels, num_classes, hidden_dim=96, num_blocks=2, num_heads=3, window_sizes=[7, 3], shift_sizes=[4, 2]):
super(SwinTransformer, self).__init__()
self.conv1 = nn.Conv2d(in_channels, hidden_dim, kernel_size=4, stride=4)
self.norm1 = nn.LayerNorm([hidden_dim, 56, 56])
self.blocks = nn.ModuleList([SwinBlock(hidden_dim, num_heads, window_sizes[i], shift_sizes[i]) for i in range(len(window_sizes))])
self.norm2 = nn.LayerNorm([hidden_dim, 56, 56])
self.conv2 = nn.Conv2d(hidden_dim, in_channels, kernel_size=1)
self.norm3 = nn.LayerNorm([in_channels, 56, 56])
self.fc = nn.Linear(in_channels * 56 * 56, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
for block in self.blocks:
x = block(x)
x = self.norm2(x)
x = self.conv2(x)
x = self.norm3(x)
x = x.flatten(start_dim=1)
x = self.fc(x)
return x
```
以上代码实现了Swin Transformer的基本结构,包括SwinBlock和SwinTransformer模块。其中SwinBlock是Swin Transformer的基本单元,而SwinTransformer则是由多个SwinBlock堆叠而成的完整模型。需要注意的是,SwinTransformer的输入需要是一个四维的张量,即(batch_size, in_channels, height, width)。
convert_dim_to_conv_op函数
我不知道您想要的是哪个深度学习框架的convert_dim_to_conv_op函数,因为不同的框架有不同的实现方式。但是,大多数深度学习框架都有类似的函数,用于将一个维度转换为卷积操作的参数。
例如,在PyTorch中,该函数被称为“unfold”,它将一个维度转换为卷积操作的参数。在TensorFlow中,该函数被称为“tf.nn.conv1d”,它也将一个维度转换为卷积操作的参数。在MXNet中,该函数被称为“mx.symbol.Convolution”,它也将一个维度转换为卷积操作的参数。
总之,convert_dim_to_conv_op函数是一个非常常见的函数,用于将一个维度转换为卷积操作的参数。无论您使用哪个深度学习框架,都可以通过查找其文档来找到这个函数的实现方式。