WCI4 4根线的含义
时间: 2024-04-25 15:25:53 浏览: 11
WCI4是一种WiFi和蓝牙共存的技术,它使用了4根线来实现WiFi和蓝牙的共存。具体来说,这4根线分别是:
1. WiFi通信线:用于传输WiFi数据的信号。
2. 蓝牙通信线:用于传输蓝牙数据的信号。
3. 共同的天线:用于同时接收WiFi和蓝牙信号。
4. 共同的地线:用于WiFi和蓝牙信号的接地。
这种4线共存的方案可以在不影响通信质量的情况下,使设备同时支持WiFi和蓝牙功能。通过使用共同的天线,可以减少设备的尺寸和成本,同时可以提高信号接收的灵敏度和性能。同时,通过使用共同的地线,可以减少信号的干扰和噪声,从而提高通信质量和稳定性。
相关问题
d3d12 wci 超大图片渲染代码实现
首先,要使用Direct3D 12进行超大图片渲染,需要使用纹理数组。纹理数组是多个相同大小和格式的纹理集合,可以同时绑定到渲染管线。这意味着,我们可以将大图片拆分成多个小纹理,并在渲染时一起使用。
接下来,我们需要将纹理数组中的每个小纹理分配到不同的图层。这可以通过使用纹理描述符来实现。在纹理描述符中,我们可以指定每个小纹理的大小、格式和图层。
接着,我们需要使用一个顶点缓冲区来描述纹理坐标和屏幕坐标之间的关系。这个顶点缓冲区包含每个小纹理的四个顶点和对应的纹理坐标。
最后,我们在渲染管线中使用纹理数组和顶点缓冲区,将所有小纹理渲染到屏幕上。在渲染时,我们可以使用视口和裁剪矩形来控制渲染区域,以避免渲染不必要的部分。
以下是示例代码:
```cpp
// 创建纹理数组
D3D12_RESOURCE_DESC textureDesc = {};
textureDesc.Dimension = D3D12_RESOURCE_DIMENSION_TEXTURE2DARRAY;
textureDesc.Width = smallTextureWidth;
textureDesc.Height = smallTextureHeight;
textureDesc.DepthOrArraySize = numTextures;
textureDesc.MipLevels = 1;
textureDesc.Format = DXGI_FORMAT_R8G8B8A8_UNORM;
textureDesc.SampleDesc.Count = 1;
textureDesc.Flags = D3D12_RESOURCE_FLAG_NONE;
// 创建顶点缓冲区
struct Vertex
{
DirectX::XMFLOAT3 position;
DirectX::XMFLOAT2 texCoord;
};
std::vector<Vertex> vertices;
for (int i = 0; i < numTextures; ++i)
{
float left = i * smallTextureWidth;
float right = left + smallTextureWidth;
float top = 0;
float bottom = smallTextureHeight;
vertices.push_back({ { left, top, 0 }, { 0, 0 } });
vertices.push_back({ { left, bottom, 0 }, { 0, 1 } });
vertices.push_back({ { right, top, 0 }, { 1, 0 } });
vertices.push_back({ { right, bottom, 0 }, { 1, 1 } });
}
// 创建视口和裁剪矩形
D3D12_VIEWPORT viewport = {};
viewport.Width = screenWidth;
viewport.Height = screenHeight;
viewport.MaxDepth = 1.0f;
D3D12_RECT scissorRect = {};
scissorRect.right = screenWidth;
scissorRect.bottom = screenHeight;
// 渲染
commandList->SetGraphicsRootSignature(rootSignature.Get());
commandList->SetPipelineState(pipelineState.Get());
commandList->RSSetViewports(1, &viewport);
commandList->RSSetScissorRects(1, &scissorRect);
commandList->IASetPrimitiveTopology(D3D_PRIMITIVE_TOPOLOGY_TRIANGLESTRIP);
commandList->IASetVertexBuffers(0, 1, &vertexBufferView);
commandList->SetGraphicsRootDescriptorTable(0, textureDescriptorHeap->GetGPUDescriptorHandleForHeapStart());
commandList->DrawInstanced(4, numTextures, 0, 0);
```
这段代码中,我们使用顶点缓冲区中的四个顶点和纹理坐标来渲染每个小纹理,使用纹理数组中的图层来指定每个小纹理。同时,我们使用视口和裁剪矩形来控制渲染区域,以避免渲染不必要的部分。
怎么直接调用pytorch中的convlstm
你可以使用以下代码来直接调用PyTorch中的ConvLSTM:
```
import torch.nn as nn
from torch.autograd import Variable
class ConvLSTMCell(nn.Module):
def __init__(self, input_size, hidden_size, kernel_size):
super(ConvLSTMCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.kernel_size = kernel_size
self.padding = kernel_size // 2
self.Wxi = nn.Conv2d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Whi = nn.Conv2d(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Wxf = nn.Conv2d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Whf = nn.Conv2d(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Wxc = nn.Conv2d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Whc = nn.Conv2d(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Wxo = nn.Conv2d(in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Who = nn.Conv2d(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding=self.padding)
self.Wci = None
self.Wcf = None
self.Wco = None
def forward(self, x, h, c):
ci = torch.sigmoid(self.Wxi(x) + self.Whi(h) + c * self.Wci)
cf = torch.sigmoid(self.Wxf(x) + self.Whf(h) + c * self.Wcf)
cc = cf * c + ci * torch.tanh(self.Wxc(x) + self.Whc(h))
co = torch.sigmoid(self.Wxo(x) + self.Who(h) + cc * self.Wco)
ch = co * torch.tanh(cc)
return ch, cc
class ConvLSTM(nn.Module):
def __init__(self, input_size, hidden_size, kernel_size, num_layers, batch_first=False):
super(ConvLSTM, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.kernel_size = kernel_size
self.num_layers = num_layers
self.batch_first = batch_first
cell_list = []
for i in range(0, self.num_layers):
cur_input_size = self.input_size if i == 0 else self.hidden_size
cell_list.append(ConvLSTMCell(input_size=cur_input_size, hidden_size=self.hidden_size, kernel_size=self.kernel_size))
self.cell_list = nn.ModuleList(cell_list)
def forward(self, input_tensor, hidden_state=None):
if hidden_state is None:
hidden_state = self._init_hidden(batch_size=input_tensor.size(0))
layer_output_list = []
last_state_list = []
seq_len = input_tensor.size(1) if self.batch_first else input_tensor.size(0)
cur_layer_input = input_tensor
for layer_idx in range(self.num_layers):
h, c = hidden_state[layer_idx]
output_inner = []
for t in range(seq_len):
h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], h=h, c=c)
output_inner.append(h)
layer_output = torch.stack(output_inner, dim=1)
cur_layer_input = layer_output
layer_output_list.append(layer_output)
last_state_list.append((h, c))
if self.batch_first:
return layer_output_list, last_state_list
else:
return layer_output_list.transpose(0, 1), last_state_list
def _init_hidden(self, batch_size):
init_states = []
for i in range(self.num_layers):
init_states.append((Variable(torch.zeros(batch_size, self.hidden_size, 64, 64)).cuda(),
Variable(torch.zeros(batch_size, self.hidden_size, 64, 64)).cuda()))
return init_states
```
这是一个ConvLSTM的实现,可以直接调用。
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